Revision o results for Urban birds’ flight responses were largely unaffected by the COVID-19 shutdowns
Martin Bulla
2023-06-15 16:34:45
Code to load tools and data
# packages
require(arm)
require(data.table)
require(effects)
require(foreach)
require(ggimage)
require(ggplot2)
require(ggpubr)
require(ggsci)
require(ggtext)
require(grid)
require(gtable)
require(here)
require(kableExtra)
require(MASS)
require(multcomp)
require(optimx)
require(performance)
require(PerformanceAnalytics)
require(png)
require(RColorBrewer)
require(rmeta)
require(rphylopic)
require(scales)
require(viridis)
# constants
save_plot = TRUE
round_ = 3 # number of decimal places to round model coefficients
nsim = 5000 # number of simulations to extract estimates and 95%CrI
ax_lines = "grey60" # defines color of the axis lines
#colors <- c("#999999", "#E69F00", "#56B4E9") #viridis(3)
set.seed(42)
#width_ = .7 # spacing between error bars
#col_ = c(brewer.pal(n =12, name = "Paired"), 'grey30','grey80')
# functions
# to add images to panels
annotation_custom2 <- function (grob, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, data) {
layer(data = data, stat = StatIdentity, position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE, params = list(grob = grob,
xmin = xmin, xmax = xmax,
ymin = ymin, ymax = ymax))
}
# to remove ggplot components
gtable_filter_remove <- function (x, name, trim = TRUE){
matches <- !(x$layout$name %in% name)
x$layout <- x$layout[matches, , drop = FALSE]
x$grobs <- x$grobs[matches]
if (trim)
x <- gtable_trim(x)
x
}
# customized ggplot theme
theme_MB = theme(
title = element_text(size=8, colour="grey30"),
axis.line = element_blank(),
#axis.line = element_line(colour="grey70", size=0.25),
axis.title = element_text(size=7, colour="grey30"),
axis.title.y = element_text(vjust=3.5),
axis.title.x = element_text(vjust=1),
axis.text = element_text(size=6),#, vjust = 0.5, hjust=1),# margin=units(0.5,"mm")),
axis.ticks.length=unit(0.5,"mm"),
axis.ticks = element_line(colour = "grey70", size = 0.1),
#axis.ticks.margin,
strip.text.x = element_text(size = 6, color="grey30", margin=margin(1,1,1,1,"mm")), #grey50
strip.text.y = element_text(size = 6, color="grey30", margin=margin(1,1,1,1,"mm")), #grey50
strip.background = element_rect(fill="grey99",colour="grey70", size=0.25),
#strip.background = element_blank(),
#strip.text = element_blank(),
panel.spacing = unit(0, "mm"),
panel.background=element_blank(),
panel.border = element_rect(colour="grey70", size=0.1, fill = NA), #panel.border=element_blank(),
panel.grid = element_blank(),
legend.text=element_text(size=6),
legend.title=element_text(size=6),
legend.key = element_rect(colour = NA, fill = NA),
legend.key.height= unit(0.5,"line"),
legend.key.width = unit(0.25, "cm"),
legend.margin = margin(0,0,0,0, unit="cm"),
legend.box.margin = margin(l = -6), #legend.justification = c(-1,0),
legend.background = element_blank()
)
# for estimates
est_out =function(model = m, label = "", nsim = 5000){
bsim = sim(model, n.sim=5000)
v = apply(bsim@fixef, 2, quantile, prob=c(0.5))
ci = apply(bsim@fixef, 2, quantile, prob=c(0.025,0.975))
sd = apply(bsim@fixef, 2, sd)
data.table(predictor=rownames(coef(summary(model))),estimate=v, lwr=ci[1,], upr=ci[2,], sd = sd, model = paste(label, "N =", nobs(model)))
}
# change color
change_col = function(replace_black, theimg) {
r_b = col2rgb(replace_black) / 255
#theimg[theimg == 1] <- 2
for (i in 1:3) {
theimg[,,i][theimg[,,i] == 0] <- r_b[i]
}
return(theimg)
}
# for Supplementary Table output based on sim
m_out = function(model = m, type = "mixed",
name = "define", dep = "define", fam = 'Gaussian',
round_ = 3, nsim = 5000, aic = FALSE, save_sim = here::here('Data/model_sim/'), back_tran = FALSE, perc_ = 1){
# perc_ 1 = proportion or 100%
bsim = sim(model, n.sim=nsim)
if(save_sim!=FALSE){save(bsim, file = paste0(save_sim, name,'.RData'))}
if(type != "mixed"){
v = apply(bsim@coef, 2, quantile, prob=c(0.5))
ci = apply(bsim@coef, 2, quantile, prob=c(0.025,0.975))
if(back_tran == TRUE & fam == "binomial"){
v = perc_*plogis(v)
ci = perc_*plogis(ci)
}
if(back_tran == TRUE & fam == "binomial_logExp"){
v = perc_*(1-plogis(v))
ci = perc_*(1-plogis(ci))
ci = rbind(ci[2,],ci[1,])
}
if(back_tran == TRUE & fam == "Poisson"){
v = exp(v)
ci = exp(ci)
}
oi=data.frame(type='fixed',effect=rownames(coef(summary(model))),estimate=v, lwr=ci[1,], upr=ci[2,])
rownames(oi) = NULL
oi$estimate_r=round(oi$estimate,round_)
oi$lwr_r=round(oi$lwr,round_)
oi$upr_r=round(oi$upr,round_)
if(perc_ == 100){
oi$estimate_r = paste0(oi$estimate_r,"%")
oi$lwr_r = paste0(oi$lwr_r,"%")
oi$upr_r = paste0(oi$upr_r,"%")
}
x=data.table(oi[c('type',"effect", "estimate_r","lwr_r",'upr_r')])
}else{
v = apply(bsim@fixef, 2, quantile, prob=c(0.5))
ci = apply(bsim@fixef, 2, quantile, prob=c(0.025,0.975))
if(back_tran == TRUE & fam == "binomial"){
v = perc_*plogis(v)
ci = perc_*plogis(ci)
}
if(back_tran == TRUE & fam == "binomial_logExp"){
v = perc_*(1-plogis(v))
ci = perc_*(1-plogis(ci))
ci = rbind(ci[2,],ci[1,])
}
if(back_tran == TRUE & fam == "Poisson"){
v = exp(v)
ci = exp(ci)
}
oi=data.table(type='fixed',effect=rownames(coef(summary(model))),estimate=v, lwr=ci[1,], upr=ci[2,])
rownames(oi) = NULL
oi[,estimate_r := round(estimate,round_)]
oi[,lwr_r := round(lwr,round_)]
oi[,upr_r :=round(upr,round_)]
if(perc_ == 100){
oi[,estimate_r := paste0(estimate_r,"%")]
oi[,lwr_r := paste0(lwr_r,"%")]
oi[,upr_r := paste0(upr_r,"%")]
}
oii=oi[,c('type',"effect", "estimate_r","lwr_r",'upr_r')]
l=data.frame(summary(model)$varcor)
l = l[is.na(l$var2),]
l$var1 = ifelse(is.na(l$var1),"",l$var1)
l$pred = paste(l$grp,l$var1)
q050={}
q025={}
q975={}
pred={}
# variance of random effects
for (ran in names(bsim@ranef)) {
ran_type = l$var1[l$grp == ran]
for(i in ran_type){
q050=c(q050,quantile(apply(bsim@ranef[[ran]][,,ran_type], 1, var), prob=c(0.5)))
q025=c(q025,quantile(apply(bsim@ranef[[ran]][,,ran_type], 1, var), prob=c(0.025)))
q975=c(q975,quantile(apply(bsim@ranef[[ran]][,,ran_type], 1, var), prob=c(0.975)))
pred= c(pred,paste(ran, i))
}
}
# residual variance
q050=c(q050,quantile(bsim@sigma^2, prob=c(0.5)))
q025=c(q025,quantile(bsim@sigma^2, prob=c(0.025)))
q975=c(q975,quantile(bsim@sigma^2, prob=c(0.975)))
pred= c(pred,'Residual')
ri=data.table(type='random %',effect=pred, estimate_r=round(100*q050/sum(q050)), lwr_r=round(100*q025/sum(q025)), upr_r=round(100*q975/sum(q975)))
ri[lwr_r>upr_r, lwr_rt := upr_r]
ri[lwr_r>upr_r, upr_rt := lwr_r]
ri[!is.na(lwr_rt), lwr_r := lwr_rt]
ri[!is.na(upr_rt), upr_r := upr_rt]
ri$lwr_rt = ri$upr_rt = NULL
ri[,estimate_r := paste0(estimate_r,'%')]
ri[,lwr_r := paste0(lwr_r,'%')]
ri[,upr_r := paste0(upr_r,'%')]
x = data.table(rbind(oii,ri))
}
x[1, model := name]
x[1, response := dep]
x[1, error_structure := fam]
N = length(resid(model))
x[1, N := N ]
x=x[ , c('model', 'response', 'error_structure', 'N', 'type',"effect", "estimate_r","lwr_r",'upr_r')]
if (aic == TRUE){
x[1, AIC := AIC(update(model,REML = FALSE))]
}
if (aic == "AICc"){
aicc = AICc(model)
x[1, AICc := aicc]
}
if(type == "mixed" & nrow(x[type=='random %' & estimate_r =='0%'])==0){
R2_mar = as.numeric(r2_nakagawa(model)$R2_marginal)
R2_con = as.numeric(r2_nakagawa(model)$R2_conditional)
x[1, R2_mar := R2_mar]
x[1, R2_con := R2_con]
}
x[is.na(x)] = ""
return(x)
}
# model assumption function
m_ass = function(name = 'define', mo = m0, dat = d, fixed = NULL, categ = NULL, trans = "none", spatial = TRUE, temporal = TRUE, PNG = TRUE, outdir = 'outdir', n_col=8, width_ = 10, height_ = 5.5){
l=data.frame(summary(mo)$varcor)
l = l[is.na(l$var2),]
nt = if(temporal==TRUE){1}else{0}
ns = if(spatial==TRUE){7}else{0}
n = 3+nrow(l)-1+length(fixed)+length(categ) + nt + ns
if(PNG == TRUE){
png(paste(outdir,name, ".png", sep=""), width=width_,height=height_,units="in",res=600) # width = 6
par(mfrow=c(4, n_col),tcl = -0.08, cex = 0.5, cex.main = 0.9,#ceiling(n/n_col),n_col)
oma = c(1,1,2,1),
mar = c(2, 2, 2, 1), mgp=c(1,0,0)
)
}else{
dev.new(width=width_,height=height_)
par(mfrow=c(4,n_col), tcl = -0.08, cex = 0.5, cex.main = 0.9,#ceiling(n/n_col),n_col)
oma = c(1,1,2,1),
mar = c(2, 2, 2, 1), mgp=c(1,0,0)
)
}
scatter.smooth(fitted(mo),resid(mo),col='grey');abline(h=0, lty=2, col ='red')
scatter.smooth(fitted(mo),sqrt(abs(resid(mo))), col='grey')
qqnorm(resid(mo), main=list("Normal Q-Q Plot: residuals"),col='grey');qqline(resid(mo), col = 'red')
#unique(l$grp[l$grp!="Residual"])
for(i in unique(l$grp[l$grp!="Residual"])){
#i = "mean_year"
ll=ranef(mo)[names(ranef(mo))==i][[1]]
if(ncol(ll)==1){
qqnorm(ll[,1], main = paste(i,names(ll)[1]),col='grey',);qqline(ll[,1], col ='red')
}else{
qqnorm(ll[,1], main = paste(i,names(ll)[1]),col='grey');qqline(ll[,1], col ='red')
qqnorm(ll[,2], main = paste(i,names(ll)[2]),col='grey');qqline(ll[,2], col ='red')
}
}
# variables
scatter={}
for (i in rownames(summary(mo)$coef)) {
#i = "lat_abs"
j=sub("\\).*", "", sub(".*\\(", "",i))
scatter[length(scatter)+1]=j
}
x = data.frame(scatter=unique(scatter)[2:length(unique(scatter))],
log_ = grepl("log",rownames(summary(mo)$coef)[2:length(unique(scatter))]), stringsAsFactors = FALSE)
for (i in 1:length(fixed)){
jj =fixed[i]
variable=dat[, ..jj][[1]]
if(trans[i]=='log'){
scatter.smooth(resid(mo)~log(variable),xlab=paste('log(',jj,')',sep=''), col = 'grey');abline(h=0, lwd=1, lty = 2, col ='red')
}else if(trans[i]=='abs'){
scatter.smooth(resid(mo)~abs(variable),xlab=paste('abs(',jj,')',sep=''), col = 'grey');abline(h=0, lwd=1, lty = 2, col ='red')
}else if(trans[i]=='sin'){scatter.smooth(resid(mo)~sin(variable),xlab=paste('sin(',jj,')',sep=''), col = 'grey');abline(h=0, lwd=1, lty = 2, col ='red')
}else if(trans[i]=='cos'){scatter.smooth(resid(mo)~cos(variable),xlab=paste('cos(',jj,')',sep=''), col = 'grey');abline(h=0, lwd=1, lty = 2, col ='red')
}else{
scatter.smooth(resid(mo)~variable,xlab=jj,col = 'grey');abline(h=0, lwd=1, lty = 2, col ='red')
}
}
if(length(categ)>0){
for(i in categ){
variable=dat[, ..i][[1]]
boxplot(resid(mo)~variable, medcol='grey', whiskcol='grey', staplecol='grey', boxcol='grey', outcol='grey', xlab = i);abline(h=0, lty=3, lwd=1, col = 'red')
}
}
if(temporal == TRUE){
acf(resid(mo), type="p", main=list("Temporal autocorrelation:\npartial series residual"))
}
if(spatial == TRUE){
spdata=data.frame(resid=resid(mo), x=dat$Lon, y=dat$Lat)
spdata$col=ifelse(spdata$resid<0,rgb(83,95,124,100, maxColorValue = 255),ifelse(spdata$resid>0,rgb(253,184,19,100, maxColorValue = 255), 'red'))
#cex_=c(1,2,3,3.5,4)
cex_=c(1,1.5,2,2.5,3)
spdata$cex=as.character(cut(abs(spdata$resid), 5, labels=cex_))
plot(spdata$x, spdata$y,col=spdata$col, cex=as.numeric(spdata$cex), pch= 16, main=list('Spatial distribution of residuals', cex=0.8), xlab = 'Longitude', ylab = 'Latitude')
legend("topright", pch=16, legend=c('>0','<0'), ,col=c(rgb(83,95,124,100, maxColorValue = 255),rgb(253,184,19,100, maxColorValue = 255)))
plot(spdata$x[spdata$resid<0], spdata$y[spdata$resid<0],col=spdata$col[spdata$resid<0], cex=as.numeric(spdata$cex[spdata$resid<0]), pch= 16, main=list('residuals <0'), xlab = 'Longitude', ylab = 'Latitude')
plot(spdata$x[spdata$resid>=0], spdata$y[spdata$resid>=0],col=spdata$col[spdata$resid>=0], cex=as.numeric(spdata$cex[spdata$resid>=0]), pch= 16, main=list('residual >=0'), xlab = 'Longitude', ylab = 'Latitude')
# EU
dat$res = resid(mo)
spdata=data.frame(resid = dat$res[dat$Country!='Australia'], x=dat$Lon[dat$Country!='Australia'], y=dat$Lat[dat$Country!='Australia'])
spdata$col=ifelse(spdata$resid<0,rgb(83,95,124,100, maxColorValue = 255),ifelse(spdata$resid>0,rgb(253,184,19,100, maxColorValue = 255), 'red'))
#cex_=c(1,2,3,3.5,4)
cex_=c(1,1.5,2,2.5,3)
spdata$cex=as.character(cut(abs(spdata$resid), 5, labels=cex_))
plot(spdata$x[spdata$resid<0], spdata$y[spdata$resid<0],col=spdata$col[spdata$resid<0], cex=as.numeric(spdata$cex[spdata$resid<0]), pch= 16, main=list('EU - residuals <0'), xlab = 'Longitude', ylab = 'Latitude')
plot(spdata$x[spdata$resid>=0], spdata$y[spdata$resid>=0],col=spdata$col[spdata$resid>=0], cex=as.numeric(spdata$cex[spdata$resid>=0]), pch= 16, main=list('EU residuals >=0)'), xlab = 'Longitude', ylab = 'Latitude')
# Australia
spdata=data.frame(resid = dat$res[dat$Country=='Australia'], x=dat$Lon[dat$Country=='Australia'], y=dat$Lat[dat$Country=='Australia'])
spdata$col=ifelse(spdata$resid<0,rgb(83,95,124,100, maxColorValue = 255),ifelse(spdata$resid>0,rgb(253,184,19,100, maxColorValue = 255), 'red'))
#cex_=c(1,2,3,3.5,4)
cex_=c(1,1.5,2,2.5,3)
spdata$cex=as.character(cut(abs(spdata$resid), 5, labels=cex_))
plot(spdata$x[spdata$resid<0], spdata$y[spdata$resid<0],col=spdata$col[spdata$resid<0], cex=as.numeric(spdata$cex[spdata$resid<0]), pch= 16, main=list('Australia residuals <0'), xlab = 'Longitude', ylab = 'Latitude')
plot(spdata$x[spdata$resid>=0], spdata$y[spdata$resid>=0],col=spdata$col[spdata$resid>=0], cex=as.numeric(spdata$cex[spdata$resid>=0]), pch= 16, main=list('Australia residuals >=0'), xlab = 'Longitude', ylab = 'Latitude')
}
mtext(stringr::str_wrap(paste(paste0(name," model: "), slot(mo,"call")[1],'(',slot(mo,"call")[2],sep=''), width = ceiling(nchar(paste(slot(mo,"call")[1],'(',slot(mo,"call")[2],sep=''))/2)+10), side = 3, line = 0, cex=0.5,outer = TRUE, col = 'darkblue') #ceiling(nchar(paste(slot(mo,"call")[1],'(',slot(mo,"call")[2],sep=''))/2)
if(PNG==TRUE){dev.off()}
}
# data
ph = fread(here::here('Data/phylopic.txt'))
setnames(ph, old = c('Name', 'Code'), new = c('genus2', 'uid'))
t = fread(here::here('Data/taxonomy.txt'))
g = fread(here::here('Data/google_mobility.txt')) #fwrite(d, here::here('Data/data.txt'), sep ='\t')
g[, Year := as.integer(substring(date, nchar(date)-3, nchar(date)))]
g[nchar(date)==9, date:=paste0('0',date)]
g[, date_ :=as.Date(date, format = '%d.%m.%Y')]
g[, Day :=yday(date_)]
g[country_region!='Australia', Day := Day-92 +1] # 1 April = start of breeding season (1st day) = 92 day of the year
g[country_region=='Australia', Day := Day-228 +1] # 15 Augusst = start of breeding season (1st day) = 228 day of the year
setnames(g, old = 'country_region', new ='Country')
d = fread(here::here('Data/data_corrected.txt')) #fwrite(d, here::here('Data/data.txt'), sep ='\t')
# add data and weekdays
x = fread(here::here("Data/date.txt"))[, .(IDObs, Date_corr)]
x[, date_:=as.Date(Date_corr, "%d.%m.%Y" )]
x[, weekday := weekdays(date_)]
d = merge(d, x[, .(IDObs, date_, weekday)], by = "IDObs")
#d[, date_ := as.Date(Day, origin = paste0(Year, '-01-01'))]
# adjust correct assignment of season (Year) for Australia
d[Country == 'Australia' & Year == 2020 & Covid == 0, Year:=2019]
d[Country == 'Australia' & Year == 2021 & Day>139, Year:=2020]
d[Country == 'Australia' & Year == 2022 & Day>139, Year:=2021]
d = d[order(Year, IDLocality, Day, Hour)]
d[Country %in% c("Czech_Republic", "Czech Republic"), Country := "Czechia"]
d[, genus := sub("_.*", "", Species)]
d[, sp_day_year := paste(Year, Species, Day, sep="_")]
d[, sp_loc := paste(Species, IDLocality, sep="_")]
d[, sp_country := paste(Species, Country, sep="\n")]
d[, rad:=(2*pi*Hour) / 24]
d[, Day_:= Day]
#d[, FID_z := scale(FID), by = Species]
#d[, SD_z := scale(SD), by = Species]
d[, FID_ln := log(FID)]
d[, SD_ln := log(SD)]
d[, body_ln := log(BodyMass)]
d[, flock_ln := log(FlockSize)]
d[, weekday := weekdays(date_)]
#d[Country == 'Australia', Day_:= abs(Day - 189)]
d1 = d[Covid == 1, .N, by = Species]
d2 = d[Covid == 0, .N, by = Species]
setnames(d1, old = 'N', new ='N_during')
setnames(d2, old = 'N', new ='N_before')
dd = merge(d1,d2) # species with data before and during
da = merge(d1,d2, all = TRUE)
d1p = d[Country!='Poland' & Covid == 1, .N, by = Species]
d2p = d[Country!='Poland' & Covid == 0, .N, by = Species]
setnames(d1p, old = 'N', new ='N_during')
setnames(d2p, old = 'N', new ='N_before')
ddp = merge(d1p,d2p) # species with data before and during, but without Poland data
p1 = d[Covid == 1, .N, by = .(IDLocality, Species)]
p2 = d[Covid == 0, .N, by = .(IDLocality, Species)]
setnames(p1, old = 'N', new ='N_during')
setnames(p2, old = 'N', new ='N_before')
pp = merge(p1,p2) # species-localities with data before and during
pa = merge(p1,p2, all = TRUE)
p1p = d[Country!='Poland' & Covid == 1, .N, by = .(IDLocality, Species)]
p2p = d[Country!='Poland' & Covid == 0, .N, by = .(IDLocality, Species)]
setnames(p1p, old = 'N', new ='N_during')
setnames(p2p, old = 'N', new ='N_before')
ppp = merge(p1p,p2p) # species-localities with data before and during,but without Poland data
p1p4 = d[Year!=2014 & Country!='Poland' & Covid == 1, .N, by = .(IDLocality, Species)]
p2p4 = d[Year!=2014 &Country!='Poland' & Covid == 0, .N, by = .(IDLocality, Species)]
setnames(p1p4, old = 'N', new ='N_during')
setnames(p2p4, old = 'N', new ='N_before')
ppp4 = merge(p1p4,p2p4) # species-localities with data before and during, but without Poland & 2014 data
s = d[Covid == 1]
s[, Nsp := .N, by ='Species']
s[, sp := gsub('[_]', ' ', Species)]
# add google mobility
s = merge(s, g[,.(Country, date_, parks_percent_change_from_baseline)], all.x = TRUE)
s[, year_ := as.character(Year)]
ss = s[!is.na(parks_percent_change_from_baseline)]
ss[, country_year := paste(Country, Year)] #table(paste(s$Country, s$Year))
ss[parks_percent_change_from_baseline<0, google := 'before_zero']
ss[parks_percent_change_from_baseline>0, google := 'after_zero']
ss[, sp_country_google:= paste(sp_country, google)]
g[, weekday := weekdays(date_)]correlations among predictors
d[, sin_rad:=sin(rad)]
d[, cos_rad := cos(rad)]
dp = d[, c("SD_ln", "flock_ln", "body_ln", "sin_rad", "cos_rad", "Temp", "Day")]
setnames(dp, old = c("SD_ln", "flock_ln", "body_ln", "sin_rad", "cos_rad", "Temp", "Day"), new = c("Starting distance\nln(m)", "Flock size\nln(m)", "Body mass\nln(m)", "Sine\nof radians", "Cosine\nof radians", "Temperature\n°C", "Day"))
chart.Correlation(dp, histogram = TRUE, pch = 19, alpha = 0.5)
mtext("Single observations", side = 3, line = 3)
if(save_plot==TRUE){
png(here::here("Outputs/Fig_S1_rev.png"), width = 19, height = 19, units = "cm", bg = "transparent", res = 600)
chart.Correlation(dp, histogram = TRUE, pch = 19, alpha = 0.5)
mtext("Single observations", side = 3, line = 3)
dev.off()
}## quartz_off_screen
## 2prediction for FID ~ Period
# predictions
# full
ms <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1| genus) + (1| Species) + (1 | sp_day_year) + (scale(Covid) | Country) + (scale(Covid) | IDLocality) + (1 | sp_loc),
data = d, REML = FALSE,
control <- lmerControl(
optimizer = "optimx", optCtrl = list(method = "nlminb")
)
)
est_ms <- est_out(ms, "All: (1|Year) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (1|sp_loc)")
est_ms[, control_for_starting_distance := "yes"]
mx <- lmer(scale(log(FID)) ~
#scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(Covid) | Country) + (scale(Covid) | IDLocality) + (1 | sp_loc),
data = d, REML = FALSE,
control = lmerControl(
optimizer ='optimx', optCtrl=list(method='nlminb'))
)
# (Covid|IDLocality) +
est_mx <- est_out(mx, "All: (1|Year) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (1|sp_loc)")
est_mx[, control_for_starting_distance := "no"]
# CZ - singular fits only due to genera estimated as zero (removing it changes no results)
cs <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1| Species) + (1 | sp_day_year) + (scale(Covid)| IDLocality) + (1|sp_loc),
#(1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = d[Country == "Czechia"], REML = FALSE
)
est_cs <- est_out(cs, "Czechia: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)+(scale(Covid)|IDLocality)+(1|sp_loc)")
est_cs[, control_for_starting_distance := "yes"]
cx <- lmer(scale(log(FID)) ~
#scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1| Species) + (1 | sp_day_year) + (scale(Covid)| IDLocality) + (1|sp_loc),
#(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = d[Country == "Czechia"], REML = FALSE
)
est_cx <- est_out(cx, "Czechia: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)+(scale(Covid)|IDLocality)+(1|sp_loc)")
est_cx[, control_for_starting_distance := "no"]
# FI
fs <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1| Species) + (1 | sp_day_year) + (scale(Covid)| IDLocality) + (1|sp_loc),
data = d[Country == "Finland"], REML = FALSE
)
est_fs <- est_out(fs, "Finland: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)+(scale(Covid)|IDLocality)+(1|sp_loc)")
est_fs[, control_for_starting_distance := "yes"]
fx <- lmer(scale(log(FID)) ~
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1| Species) + (1 | sp_day_year) + (scale(Covid)| IDLocality) + (1|sp_loc),
data = d[Country == "Finland"], REML = FALSE
)
est_fx <- est_out(fx, "Finland: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)+(scale(Covid)|IDLocality)+(1|sp_loc)")
est_fx[, control_for_starting_distance := "no"]
# HU - singular fits only due to sp_loc estimated as zero (removing it changes no results)
hs <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(Covid) | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = d[Country == "Hungary"], REML = FALSE
)
est_hs <- est_out(hs, "Hungary: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)+(scale(Covid)|IDLocality)+(1|sp_loc)")
est_hs[, control_for_starting_distance := "yes"]
hx <- lmer(scale(log(FID)) ~
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(Covid) | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = d[Country == "Hungary"], REML = FALSE
)
est_hx <- est_out(hx, "Hungary: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)+(scale(Covid)|IDLocality)+(1|sp_loc)")
est_hx[, control_for_starting_distance := "no"]
# AU - singular fits only due to Year and random slope estimated as zero (removing those changes no results)
as <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) +(1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(Covid) | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = d[Country == "Australia"], REML = FALSE
)
est_as <- est_out(as, "Australia: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)+(scale(Covid)|IDLocality)+(1|sp_loc)")
est_as[, control_for_starting_distance := "yes"]
ax <- lmer(scale(log(FID)) ~
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(Covid) | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = d[Country == "Australia"], , REML = FALSE,
control = lmerControl(
optimizer ='optimx', optCtrl=list(method='nlminb'))
)
est_ax <- est_out(ax, "Australia: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)+(scale(Covid)|IDLocality)+(1|sp_loc)")
est_ax[, control_for_starting_distance := "no"]
# PL
ps <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = d[Country == "Poland"], REML = FALSE
)
est_ps <- est_out(ps, "Poland: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)")
est_ps[, control_for_starting_distance := "yes"]
px <- lmer(scale(log(FID)) ~
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = d[Country == "Poland"], REML = FALSE
)
est_px <- est_out(px, "Poland: (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year)")
est_px[, control_for_starting_distance := "no"]
# combine
est_ms[, Country := 'All\n(mixed model)']
est_mx[, Country := "All\n(mixed model)"]
est_as[, Country := "Australia"]
est_ax[, Country := "Australia"]
est_cs[, Country := "Czechia"]
est_cx[, Country := "Czechia"]
est_hs[, Country := "Hungary"]
est_hx[, Country := "Hungary"]
est_ps[, Country := "Poland"]
est_px[, Country := "Poland"]
est_fs[, Country := "Finland"]
est_fx[, Country := "Finland"]
o = rbind(est_ms, est_mx,
est_as, est_ax,
est_cs, est_cx,
est_hs, est_hx,
est_ps, est_px,
est_fs, est_fx)
save(o, file = here::here('Data/dat_est_rev.Rdata'))
#AIC(hs, hx)
#AIC(cs, cx)
#0AIC(ps, px)
#AIC(as, ax)
#AIC(fs, fx)
# supplementary Sable S_fid_period
ms_out <- m_out(name = "Table S_FID_country - full a", dep = "Escape distance", model = ms, nsim = 5000)
mx_out <- m_out(name = "Table S_FID_country - full b", dep = "Escape distance", model = mx, nsim = 5000)
cs_out <- m_out(name = "Table S_FID_country - CZ a", dep = "Escape distancey", model = cs, nsim = 5000)
cx_out <- m_out(name = "Table S_FID_country - CZ b", dep = "Escape distance", model = cx, nsim = 5000)
fs_out <- m_out(name = "Table S_FID_country - FI a", dep = "Escape distance", model = fs, nsim = 5000)
fx_out <- m_out(name = "Table S_FID_country - FI b", dep = "Escape distance", model = fx, nsim = 5000)
hs_out <- m_out(name = "Table S_FID_country - HU a", dep = "Escape distance", model = hs, nsim = 5000)
hx_out <- m_out(name = "Table S_FID_country - HU b", dep = "Escape distance", model = hx, nsim = 5000)
as_out <- m_out(name = "Table S_FID_country - AU a", dep = "Escape distance", model = as, nsim = 5000)
ax_out <- m_out(name = "Table S_FID_country - AU b", dep = "Escape distance", model = ax, nsim = 5000)
ps_out <- m_out(name = "Table S_FID_country - PL a", dep = "Escape distance", model = ps, nsim = 5000)
px_out <- m_out(name = "Table S_FID_country - PL b", dep = "Escape distance", model = px, nsim = 5000)
out_FID_c <- rbind(fs_out, fx_out, ps_out, px_out, cs_out, cx_out, hs_out, hx_out, as_out, ax_out, fill = TRUE)
out_FID_c[is.na(out_FID_c)] <- ""
out_FID_c[, effect := gsub("scale\\(Covid\\)", "Period", effect)]
out_FID_c[, effect := gsub("Year", "year", effect)]
out_FID_c[, effect := gsub("scale\\(SD\\)", "starting distance", effect)]
out_FID_c[, effect := gsub("Temp", "temperaturre", effect)]
out_FID_c[, effect := gsub("FlockSize", "flock size", effect)]
out_FID_c[, effect := gsub("BodyMass", "body mass", effect)]
out_FID_c[, effect := gsub("Species", "species", effect)]
out_FID_c[, effect := gsub("sp_day_year", "species within day & year", effect)]
out_FID_c[, effect := gsub("IDLocality", "site", effect)]
out_FID_c[, effect := gsub("sp_loc", "species within site", effect)]
fwrite(file = here::here("Outputs/Table_S_FID_c.csv"), out_FID_c)load(here::here("Data/dat_est_rev.Rdata"))
o[predictor %in% c("scale(Covid)"), predictor := "Period"]
oo <- o[predictor %in% c("Period")]
oo[, N:=as.numeric(sub('.*N = ', '', model))]
# add meta-analytical mean
oo_s = oo[control_for_starting_distance == 'yes']
met = summary(meta.summaries(d = oo_s$estimate, se = oo_s$sd, method = "fixed", weights = oo_s$N))$summci
oo_met = data.table(predictor = "Period", estimate = met[2], lwr = met[1], upr = met[3], sd = NA, model = NA, control_for_starting_distance = "yes", Country = "Combined\n(metanalytical)", N = NA)
oo_sx = oo[control_for_starting_distance == "no"]
metx = summary(meta.summaries(d = oo_sx$estimate, se = oo_sx$sd, method = "fixed", weights = oo_sx$N))$summci
oo_metx = data.table(predictor = "Period", estimate = metx[2], lwr = metx[1], upr = metx[3], sd = NA, model = NA, control_for_starting_distance = "no", Country = "Combined\n(metanalytical)", N = NA)
oo = rbind(oo, oo_met, oo_metx)
oo[, Country := factor(Country, levels = rev(c("Finland", "Poland", "Czechia", "Hungary", "Australia", "Combined\n(metanalytical)", "All\n(mixed model)")))]
width_ <- .5 # spacing between error bars
#col_ <- c(brewer.pal(n = 12, name = "Paired"), "grey30", "grey80")
#Tol_bright <- c("#EE6677", "#228833", "#4477AA", "#CCBB44", "#66CCEE", "#AA3377", "#BBBBBB")
#Tol_muted <- c("#88CCEE", "#44AA99", "#117733", "#332288", "#DDCC77", "#999933", "#CC6677", "#882255", "#AA4499", "#DDDDDD")
#Tol_light <- c("#BBCC33", "#AAAA00", "#77AADD", "#EE8866", "#EEDD88", "#FFAABB", "#99DDFF", "#44BB99", "#DDDDDD")
# From Color Universal Design (CUD): https://jfly.uni-koeln.de/color/
#Okabe_Ito <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#000000")
#col_ = Okabe_Ito[7:1]
# JAMA and LocusZoom modified order
#col_ = c("#374E55FF", "#374E55FF", "#DF8F44FF", "#79AF97FF", "#00A1D5FF", "#B24745FF", "#80796BFF") #"#6A6599FF",
#col_ <- c("#357EBDFF", "#9632B8FF", "#46B8DAFF", "#5CB85CFF", "#EEA236FF", "#D43F3AFF", "#D43F3AFF")[7:1] # "#D43F3AFF", "#B8B8B8FF"
col_ = c("#357EBDFF", "#D43F3AFF", "#46B8DAFF", "#5CB85CFF", "#EEA236FF", "#9632B8FF", "#9632B8FF")[7:1] # "#D43F3AFF", "#B8B8B8FF"
#show_col(col_)
#p =
ggplot(oo, aes(x = estimate, y = Country, col = Country, shape = control_for_starting_distance)) +
geom_vline(xintercept = 0, color = "grey", linetype = "dotted") +
geom_errorbarh(aes(xmin = lwr, xmax = upr), height = 0, position = ggstance::position_dodgev(width_)) +
# geom_point(position = ggstance::position_dodgev(.6)) +
geom_point(position = position_dodge(width = width_), bg = "white", size = 1.1) +
# scale_color_viridis(discrete=TRUE, begin=0, end = 0.5) +
# scale_fill_viridis(discrete=TRUE, begin=0, end = 0.5) +
# geom_text( aes(x = n_pos,label = N), vjust = 0, size = 1.75, position = ggstance::position_dodgev(width_))+ # 3 positions for 3 bars
# annotate("text", x=log10(3), y=85, label= "Used", col = "grey30", size = 2.5)+
scale_shape_manual(name = "Controlled for\nstarting distance", guide = guide_legend(reverse = TRUE), values = c(21, 19)) +
#scale_color_jama(guide = "none")+ #, palette = 'light'
scale_color_manual(guide = "none", values = col_) + #guide_legend(reverse = TRUE)
scale_x_continuous(breaks = round(seq(-0.6, 1.2, by = 0.3), 1)) +
ylab("") +
xlab("Standardized effect size of period\n[on flight initiation distance]") +
# coord_cartesian(xlim = c(-.15, .15)) +
# scale_x_continuous(breaks = round(seq(-.15, .15, by = 0.05),2)) +
theme_bw() +
theme(
legend.position = "right",
legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
# legend.spacing.y = unit(0.1, 'cm'),
legend.key.height = unit(0.5, "line"),
legend.margin = margin(0, 0, 0, 0),
# legend.position=c(0.5,1.6),
plot.title = element_text(color = "grey", size = 7),
plot.margin = margin(b = 0.5, l = 0.5, t = 0.5, r = 0.5, unit = "pt"),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = ax_lines, size = 0.25),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.x = element_line(colour = ax_lines, size = 0.25),
# axis.text.x = element_text()
axis.ticks.length = unit(1, "pt"),
axis.text.x = element_text(, size = 6),
axis.text.y = element_text(colour = "black", size = 7),
axis.title = element_text(size = 7)
)
ggsave(here::here("Outputs/Fig_rev_width_CustomLocusZoom_v2.png"), width = 8, height = 6, unit = "cm", dpi = 600)
#ggsave("Outputs/Fig_rev_width_CustomJAMAv1.png", width = 8, height = 6, unit = "cm", dpi = 600)
#ggsave("Outputs/Fig_rev_width_Okabe_v2.png", width = 8, height = 6, unit = "cm", dpi = 600)
#ggsave("Outputs/Fig_rev_width_UChicago_v3.png", width = 8, height = 6, unit = "cm", dpi = 600)
#ggsave("Outputs/Fig_rev_width_d3_v2.png", width = 8, height = 6, unit = "cm", dpi = 600)
#ggsave("Outputs/Fig_rev_width_nejm_v2.png", width = 8, height = 6, unit = "cm", dpi = 600)
#ggsave("Outputs/Fig_rev_width_jama_v2.png", width = 8, height = 6, unit = "cm", dpi = 600)
#ggsave("Outputs/Fig_rev_width_jco_v2.png", width = 8, height = 6, unit = "cm", dpi = 600)
#ggsave("Outputs/Fig_rev_width_npg_v2.png", width = 8, height = 6, unit = "cm", dpi = 600)
# show used colors
#gg <- ggplot_build(p)
#col_ = unique(gg$data[[3]]["colour"])$colour
#show_col(col_)Fig. 1 | The effect size with 95%CIs of Period (0 – before, 1 – during shutdowns) on flight initiation distance (ln-transformed). The general model on all data was controlled for starting distance (ln-transformed; filled circles) or not (empty circles), flock size (ln-transformed), temperature (also a proxy for a day within the breeding season: r Pearson = 0.48; Fig. S1) and time of day. To account for circular properties of time, time was transformed into radians (2 × time × π/24) and fitted as sine and cosine of radians (Bulla et al. 2016). All continuous variables were standardised by subtracting the mean and dividing by the standard deviation. The multicollinearity was small as correlations between predictors were weak (r<XX, Fig. S1). To account for the non-independence of data points (Schielzeth & Forstmeier 2009; Barr et al. 2013), we attempted to fit random intercepts of year, weekday, genus, species, species at a given day and year, country, site, and species within a site, while fitting Period as random slope within site. Note that fitting Period as random slope at other random intercepts produces similar results (see Fig Sxx). We used this approach with a full dataset with all observations (n = 6369), as well as with conservative datasets, one with at least five observations per species and Period (i.e. at least five observations before and five during the COVID-19 shutdowns; n = 5260), the other with at least 10 observations per species and each Period (n = 5106). In other words, conservative datasets included species sampled in the both Periods. Albeit random structure of the full model accounts for the potential country specific effect (country accounted for xx% of variance, Table xx), depicted are also estimates from country specific models, with same predictors and random structure as the full model (excluding the random intercept of country and in case of Poland also site, as specific sites were not noted in Poland). Using meta.summaries function from rmeta R-package we used the country estimates, their standard deviation, and sample size per country to estimate the meta-analytical mean, which reflects the results based on the full mixed effect model. Also, albeit the response of humans to shutdowns were likely country specific and presence of humans in parks might have increased in some countries, decreased in others or did not change, the escape distances do not reflect such changes. In other words, the birds are either inflexible or the change in human behavior due to shutdowns was not strong enought, which might have been the case - see Fig. ZZ - decide what to show in this figure (I think 2020-2022 changes in Google Mobility for each country including the fits and refering to supplementary figure)
Table FID_c | Escape distance in relations to Period, given country
out_FID_c$R2_mar = out_FID_c$R2_con = NULL
out_FID_c %>%
kbl() %>%
kable_paper("hover", full_width = F)| model | response | error_structure | N | type | effect | estimate_r | lwr_r | upr_r |
|---|---|---|---|---|---|---|---|---|
| Table S_FID_country - FI a | Escape distance | Gaussian | 1019 | fixed | (Intercept) | -0.035 | -0.321 | 0.231 |
| fixed | scale(log(SD)) | 0.282 | 0.228 | 0.336 | ||||
| fixed | scale(log(flock size)) | -0.049 | -0.101 | 0.001 | ||||
| fixed | scale(log(body mass)) | 0.091 | -0.066 | 0.24 | ||||
| fixed | scale(sin(rad)) | 0.085 | 0.022 | 0.147 | ||||
| fixed | scale(cos(rad)) | 0.031 | -0.028 | 0.088 | ||||
| fixed | scale(temperaturre) | -0.079 | -0.148 | -0.009 | ||||
| fixed | Period | -0.15 | -0.337 | 0.031 | ||||
| random % | species within day & year (Intercept) | 10% | 9% | 11% | ||||
| random % | species within site (Intercept) | 6% | 5% | 7% | ||||
| random % | site (Intercept) | 1% | 0% | 3% | ||||
| random % | site Period | 1% | 0% | 3% | ||||
| random % | species (Intercept) | 4% | 3% | 5% | ||||
| random % | genus (Intercept) | 10% | 7% | 14% | ||||
| random % | weekday (Intercept) | 0% | 0% | 1% | ||||
| random % | year (Intercept) | 5% | 1% | 11% | ||||
| random % | Residual | 61% | 50% | 72% | ||||
| Table S_FID_country - FI b | Escape distance | Gaussian | 1019 | fixed | (Intercept) | -0.125 | -0.468 | 0.221 |
| fixed | scale(log(flock size)) | -0.018 | -0.07 | 0.034 | ||||
| fixed | scale(log(body mass)) | 0.2 | 0.03 | 0.375 | ||||
| fixed | scale(sin(rad)) | 0.093 | 0.026 | 0.161 | ||||
| fixed | scale(cos(rad)) | 0.029 | -0.032 | 0.089 | ||||
| fixed | scale(temperaturre) | -0.076 | -0.148 | -0.005 | ||||
| fixed | Period | -0.132 | -0.366 | 0.101 | ||||
| random % | species within day & year (Intercept) | 9% | 7% | 10% | ||||
| random % | species within site (Intercept) | 7% | 6% | 7% | ||||
| random % | site (Intercept) | 1% | 1% | 2% | ||||
| random % | site Period | 1% | 1% | 2% | ||||
| random % | species (Intercept) | 7% | 5% | 8% | ||||
| random % | genus (Intercept) | 10% | 7% | 13% | ||||
| random % | weekday (Intercept) | 1% | 0% | 1% | ||||
| random % | year (Intercept) | 9% | 2% | 16% | ||||
| random % | Residual | 56% | 44% | 68% | ||||
| Table S_FID_country - PL a | Escape distance | Gaussian | 762 | fixed | (Intercept) | 0.145 | -0.115 | 0.404 |
| fixed | scale(log(SD)) | 0.479 | 0.419 | 0.538 | ||||
| fixed | scale(log(flock size)) | 0.012 | -0.042 | 0.066 | ||||
| fixed | scale(log(body mass)) | -0.07 | -0.172 | 0.031 | ||||
| fixed | scale(sin(rad)) | -0.035 | -0.117 | 0.05 | ||||
| fixed | scale(cos(rad)) | -0.012 | -0.101 | 0.078 | ||||
| fixed | scale(temperaturre) | -0.111 | -0.182 | -0.04 | ||||
| fixed | Period | 0.119 | -0.112 | 0.353 | ||||
| random % | species within day & year (Intercept) | 8% | 8% | 8% | ||||
| random % | species (Intercept) | 15% | 12% | 17% | ||||
| random % | genus (Intercept) | 0% | 0% | 0% | ||||
| random % | weekday (Intercept) | 0% | 0% | 1% | ||||
| random % | year (Intercept) | 11% | 7% | 17% | ||||
| random % | Residual | 66% | 57% | 72% | ||||
| Table S_FID_country - PL b | Escape distance | Gaussian | 762 | fixed | (Intercept) | 0.246 | -0.075 | 0.558 |
| fixed | scale(log(flock size)) | 0.054 | -0.009 | 0.117 | ||||
| fixed | scale(log(body mass)) | 0.046 | -0.097 | 0.19 | ||||
| fixed | scale(sin(rad)) | -0.014 | -0.112 | 0.077 | ||||
| fixed | scale(cos(rad)) | -0.001 | -0.104 | 0.099 | ||||
| fixed | scale(temperaturre) | -0.104 | -0.187 | -0.02 | ||||
| fixed | Period | 0.245 | 0.001 | 0.498 | ||||
| random % | species within day & year (Intercept) | 11% | 10% | 11% | ||||
| random % | species (Intercept) | 26% | 22% | 29% | ||||
| random % | genus (Intercept) | 1% | 1% | 2% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | year (Intercept) | 8% | 5% | 14% | ||||
| random % | Residual | 53% | 46% | 60% | ||||
| Table S_FID_country - CZ a | Escape distancey | Gaussian | 2013 | fixed | (Intercept) | 0.111 | -0.257 | 0.481 |
| fixed | scale(log(SD)) | 0.392 | 0.351 | 0.437 | ||||
| fixed | scale(log(flock size)) | 0.005 | -0.03 | 0.037 | ||||
| fixed | scale(log(body mass)) | 0.17 | 0.038 | 0.308 | ||||
| fixed | scale(sin(rad)) | 0.05 | 0.002 | 0.099 | ||||
| fixed | scale(cos(rad)) | 0.054 | 0.009 | 0.097 | ||||
| fixed | scale(temperaturre) | 0.043 | -0.008 | 0.093 | ||||
| fixed | Period | 0.036 | -0.294 | 0.361 | ||||
| random % | species within day & year (Intercept) | 3% | 2% | 3% | ||||
| random % | species within site (Intercept) | 3% | 2% | 3% | ||||
| random % | species (Intercept) | 13% | 12% | 13% | ||||
| random % | site (Intercept) | 2% | 0% | 3% | ||||
| random % | site Period | 2% | 0% | 3% | ||||
| random % | genus (Intercept) | 12% | 11% | 11% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | year (Intercept) | 15% | 1% | 33% | ||||
| random % | Residual | 52% | 34% | 70% | ||||
| Table S_FID_country - CZ b | Escape distance | Gaussian | 2013 | fixed | (Intercept) | -0.043 | -0.969 | 0.867 |
| fixed | scale(log(flock size)) | 0.005 | -0.031 | 0.04 | ||||
| fixed | scale(log(body mass)) | 0.231 | 0.078 | 0.382 | ||||
| fixed | scale(sin(rad)) | 0.14 | 0.086 | 0.195 | ||||
| fixed | scale(cos(rad)) | 0.123 | 0.075 | 0.172 | ||||
| fixed | scale(temperaturre) | 0.035 | -0.027 | 0.098 | ||||
| fixed | Period | 0.223 | -0.642 | 1.1 | ||||
| random % | species within day & year (Intercept) | 2% | 1% | 5% | ||||
| random % | species within site (Intercept) | 1% | 1% | 2% | ||||
| random % | species (Intercept) | 6% | 4% | 10% | ||||
| random % | site (Intercept) | 1% | -1% | 1% | ||||
| random % | site Period | 1% | -1% | 1% | ||||
| random % | genus (Intercept) | 8% | 5% | 11% | ||||
| random % | weekday (Intercept) | 1% | 0% | 1% | ||||
| random % | year (Intercept) | 54% | 17% | 73% | ||||
| random % | Residual | 26% | 13% | 57% | ||||
| Table S_FID_country - HU a | Escape distance | Gaussian | 1456 | fixed | (Intercept) | 0.097 | -0.174 | 0.384 |
| fixed | scale(log(SD)) | 0.485 | 0.438 | 0.531 | ||||
| fixed | scale(log(flock size)) | -0.012 | -0.051 | 0.031 | ||||
| fixed | scale(log(body mass)) | -0.065 | -0.184 | 0.059 | ||||
| fixed | scale(sin(rad)) | -0.077 | -0.133 | -0.019 | ||||
| fixed | scale(cos(rad)) | -0.011 | -0.06 | 0.037 | ||||
| fixed | scale(temperaturre) | -0.023 | -0.083 | 0.037 | ||||
| fixed | Period | -0.091 | -0.271 | 0.097 | ||||
| random % | species within day & year (Intercept) | 5% | 4% | 5% | ||||
| random % | species within site (Intercept) | 0% | 0% | 0% | ||||
| random % | species (Intercept) | 3% | 2% | 3% | ||||
| random % | genus (Intercept) | 7% | 5% | 8% | ||||
| random % | site (Intercept) | 2% | 0% | 8% | ||||
| random % | site Period | 2% | 0% | 8% | ||||
| random % | weekday (Intercept) | 1% | 0% | 1% | ||||
| random % | year (Intercept) | 5% | 2% | 8% | ||||
| random % | Residual | 76% | 59% | 84% | ||||
| Table S_FID_country - HU b | Escape distance | Gaussian | 1456 | fixed | (Intercept) | 0.078 | -0.336 | 0.494 |
| fixed | scale(log(flock size)) | -0.011 | -0.057 | 0.035 | ||||
| fixed | scale(log(body mass)) | 0.187 | 0.006 | 0.376 | ||||
| fixed | scale(sin(rad)) | -0.111 | -0.18 | -0.044 | ||||
| fixed | scale(cos(rad)) | -0.007 | -0.06 | 0.05 | ||||
| fixed | scale(temperaturre) | -0.025 | -0.095 | 0.043 | ||||
| fixed | Period | -0.105 | -0.307 | 0.109 | ||||
| random % | species within day & year (Intercept) | 7% | 5% | 8% | ||||
| random % | species within site (Intercept) | 0% | 0% | 0% | ||||
| random % | species (Intercept) | 2% | 2% | 2% | ||||
| random % | genus (Intercept) | 18% | 13% | 18% | ||||
| random % | site (Intercept) | 1% | 0% | 12% | ||||
| random % | site Period | 1% | 0% | 12% | ||||
| random % | weekday (Intercept) | 1% | 0% | 1% | ||||
| random % | year (Intercept) | 5% | 3% | 7% | ||||
| random % | Residual | 63% | 42% | 74% | ||||
| Table S_FID_country - AU a | Escape distance | Gaussian | 1119 | fixed | (Intercept) | -0.064 | -0.202 | 0.081 |
| fixed | scale(log(SD)) | 0.502 | 0.452 | 0.553 | ||||
| fixed | scale(log(flock size)) | 0.02 | -0.026 | 0.068 | ||||
| fixed | scale(log(body mass)) | -0.066 | -0.163 | 0.029 | ||||
| fixed | scale(sin(rad)) | -0.032 | -0.099 | 0.036 | ||||
| fixed | scale(cos(rad)) | -0.026 | -0.093 | 0.041 | ||||
| fixed | scale(temperaturre) | 0.009 | -0.045 | 0.063 | ||||
| fixed | Period | -0.009 | -0.08 | 0.062 | ||||
| random % | species within day & year (Intercept) | 7% | 6% | 7% | ||||
| random % | species within site (Intercept) | 13% | 11% | 13% | ||||
| random % | species (Intercept) | 14% | 11% | 14% | ||||
| random % | genus (Intercept) | 2% | 1% | 2% | ||||
| random % | site (Intercept) | 0% | -1% | 8% | ||||
| random % | site Period | 0% | -1% | 8% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | year (Intercept) | 0% | 0% | 0% | ||||
| random % | Residual | 64% | 51% | 68% | ||||
| Table S_FID_country - AU b | Escape distance | Gaussian | 1119 | fixed | (Intercept) | -0.18 | -0.432 | 0.058 |
| fixed | scale(log(flock size)) | 0.051 | -0.002 | 0.104 | ||||
| fixed | scale(log(body mass)) | 0.055 | -0.073 | 0.184 | ||||
| fixed | scale(sin(rad)) | -0.017 | -0.098 | 0.063 | ||||
| fixed | scale(cos(rad)) | -0.006 | -0.08 | 0.069 | ||||
| fixed | scale(temperaturre) | 0.004 | -0.058 | 0.067 | ||||
| fixed | Period | 0.156 | -0.007 | 0.331 | ||||
| random % | species within day & year (Intercept) | 8% | 5% | 10% | ||||
| random % | species within site (Intercept) | 12% | 8% | 15% | ||||
| random % | species (Intercept) | 17% | 14% | 17% | ||||
| random % | genus (Intercept) | 5% | 4% | 5% | ||||
| random % | site (Intercept) | 1% | -10% | 15% | ||||
| random % | site Period | 1% | -10% | 15% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | year (Intercept) | 1% | 0% | 2% | ||||
| random % | Residual | 55% | 36% | 73% |
Continuous variables were z-transformed.
Exploration of Google Mobility
g_ <- fread(here::here("Data/google_mobility.txt")) # fwrite(d, here::here('Data/data.txt'), sep ='\t')
g_[, Year := as.integer(substring(date, nchar(date) - 3, nchar(date)))]
g_[nchar(date) == 9, date := paste0("0", date)]
g_[, date_ := as.Date(date, format = "%d.%m.%Y")]
g_[, Day := yday(date_)]
setnames(g_, old = "country_region", new = "Country")
g_[, weekday := weekdays(date_)]
g0 = ggplot(g_, aes(x = parks_percent_change_from_baseline, fill = factor(Year))) +
geom_histogram(position = "dodge") +
# scale_y_continuous(trans = 'log')+
scale_fill_manual(values = c("orange", "skyblue", "black"), guide = 'none') +
geom_vline(xintercept = 0, lty = 3, col = "red") +
labs(subtitle = "Distribution") +
xlab( 'Google Mobility\n[% change in human presence]') +
ylab( 'Count') +
facet_wrap(~Country, nrow = 5)
g1 = ggplot(g_, aes(x = Day, y = parks_percent_change_from_baseline, col = factor(Year))) +
geom_line() +
facet_wrap(~Country, nrow = 5) +
labs(subtitle = "Raw data", xlab = 'Day\n ', y = 'Google Mobility\n[% change in human presence]') +
# scale_y_continuous(trans = 'log')+
coord_cartesian(ylim = c(-100, 300))+
scale_color_manual(values = c("orange", "skyblue", "black"), guide = 'none')
g2 = ggplot(g_, aes(x = Day, y = parks_percent_change_from_baseline, col = factor(Year))) +
stat_smooth() +
facet_wrap(~Country, nrow = 5) +
labs(subtitle = "Loess", xlab = 'Day\n ') +
# scale_y_continuous(trans = 'log')+
coord_cartesian(ylim = c(-100, 300))+
scale_color_manual(values = c("orange", "skyblue", "black"), name = 'Year')+
theme(
axis.text.y = element_blank(),
axis.title.y = element_blank()
)
ggarrange(
g0, g1, g2,
ncol = 3, widths = c(0.9, 1, 1.1)
)
ggsave(here::here("Outputs/Fig_ZZ.png"), width = 8*2.54, height = 5*2.54, unit = "cm", dpi = 600)Fig. ZZ | Changes in human presence (Google Mobility) in parks across year and between years. Left plots represent the raw data, right plots LOESS smoothed curves. Google Mobility is absent for years before COVID-19. Nevertheless, 2022 was a year without shutdowns in the studied countries. Assuming that human activities levels might have been similar to pre-COVID-19 years (which may not be the case), human acctivity in the shutdown years (2020 and 2021) decreased. However, such decrease might be irrelevant for birds as the day-to-day variatioin in human presence seems larger than the general decrease in activity. For weekday specific plot see Fig. S_ZZ
g[, weekday := factor(weekday, levels = (c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")))]
ggplot(g, aes(x = Day, y = parks_percent_change_from_baseline, col = factor(Year))) +
geom_line() +
facet_grid(rows = vars(Country), cols = vars(weekday)) +
# scale_y_continuous(trans = 'log')+
scale_color_manual(values = c("orange", "skyblue", "black"))
Fig. S_ZZ | Changes in human presence (Google Mobility) in parks across weekdays and years. Depicted are raw data.
Google Mobility vs Stringency
# Predictions
l = list()
sc = s[Country == "Czechia"]
cz <- lmer(parks_percent_change_from_baseline ~
StringencyIndex +
(scale(StringencyIndex)|weekday),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = sc, REML = FALSE
)
bsim <- sim(cz, n.sim = nsim)
v <- apply(bsim@fixef, 2, quantile, prob = c(0.5))
newD <- data.frame(StringencyIndex = seq(min(sc$StringencyIndex), max(sc$StringencyIndex), length.out = 100)) # values to predict for
X <- model.matrix(~StringencyIndex, data = newD) # exactly the model which was used has to be specified here
newD$pred <- (X %*% v)
predmatrix <- matrix(nrow = nrow(newD), ncol = nsim)
for (j in 1:nsim) predmatrix[, j] <- (X %*% bsim@fixef[j, ])
newD$lwr <- apply(predmatrix, 1, quantile, prob = 0.025)
newD$upr <- apply(predmatrix, 1, quantile, prob = 0.975)
newD$pred <- apply(predmatrix, 1, quantile, prob = 0.5)
newD$Country = 'Czechia'
l[[1]] = newD
s[, year_weekday :=paste(Year, weekday)]
sf = s[Country == "Finland"]
fi <- lmer(parks_percent_change_from_baseline ~
Year+
StringencyIndex +
(scale(StringencyIndex)|year_weekday),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = sf, REML = FALSE
)
bsim <- sim(fi, n.sim = nsim)
v <- apply(bsim@fixef, 2, quantile, prob = c(0.5))
newD <- data.frame(Year = mean(sf$Year), StringencyIndex = seq(min(sf$StringencyIndex), max(sf$StringencyIndex), length.out = 100)) # values to predict for
X <- model.matrix(~Year + StringencyIndex, data = newD) # exactly the model which was used has to be specified here
newD$pred <- (X %*% v)
predmatrix <- matrix(nrow = nrow(newD), ncol = nsim)
for (j in 1:nsim) predmatrix[, j] <- (X %*% bsim@fixef[j, ])
newD$lwr <- apply(predmatrix, 1, quantile, prob = 0.025)
newD$upr <- apply(predmatrix, 1, quantile, prob = 0.975)
newD$pred <- apply(predmatrix, 1, quantile, prob = 0.5)
newD$Country = 'Finland'
newD$Year = NULL
l[[2]] = newD
sh <- s[Country == "Hungary"]
hu <- lmer(parks_percent_change_from_baseline ~
Year +
StringencyIndex +
(scale(StringencyIndex) | year_weekday),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = sh, REML = FALSE
)
bsim <- sim(hu, n.sim = nsim)
v <- apply(bsim@fixef, 2, quantile, prob = c(0.5))
newD <- data.frame(Year = mean(sh$Year), StringencyIndex = seq(min(sh$StringencyIndex), max(sh$StringencyIndex), length.out = 100)) # values to predict for
X <- model.matrix(~ Year + StringencyIndex, data = newD) # exactly the model which was used has to be specified here
newD$pred <- (X %*% v)
predmatrix <- matrix(nrow = nrow(newD), ncol = nsim)
for (j in 1:nsim) predmatrix[, j] <- (X %*% bsim@fixef[j, ])
newD$lwr <- apply(predmatrix, 1, quantile, prob = 0.025)
newD$upr <- apply(predmatrix, 1, quantile, prob = 0.975)
newD$pred <- apply(predmatrix, 1, quantile, prob = 0.5)
newD$Country <- "Hungary"
newD$Year <- NULL
l[[3]] <- newD
sp <- s[Country == "Poland"]
pl <- lmer(parks_percent_change_from_baseline ~
Year +
StringencyIndex +
(scale(StringencyIndex) | year_weekday),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = sp, REML = FALSE
)
bsim <- sim(pl, n.sim = nsim)
v <- apply(bsim@fixef, 2, quantile, prob = c(0.5))
newD <- data.frame(Year = mean(sp$Year), StringencyIndex = seq(min(sp$StringencyIndex), max(sp$StringencyIndex), length.out = 100)) # values to predict for
X <- model.matrix(~ Year + StringencyIndex, data = newD) # exactly the model which was used has to be specified here
newD$pred <- (X %*% v)
predmatrix <- matrix(nrow = nrow(newD), ncol = nsim)
for (j in 1:nsim) predmatrix[, j] <- (X %*% bsim@fixef[j, ])
newD$lwr <- apply(predmatrix, 1, quantile, prob = 0.025)
newD$upr <- apply(predmatrix, 1, quantile, prob = 0.975)
newD$pred <- apply(predmatrix, 1, quantile, prob = 0.5)
newD$Country <- "Poland"
newD$Year <- NULL
l[[4]] <- newD
sa <- s[Country == "Australia"]
au <- lmer(parks_percent_change_from_baseline ~
Year +
StringencyIndex +
(scale(StringencyIndex) | year_weekday),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = sa, REML = FALSE
)
bsim <- sim(au, n.sim = nsim)
v <- apply(bsim@fixef, 2, quantile, prob = c(0.5))
newD <- data.frame(Year = mean(sa$Year), StringencyIndex = seq(min(sa$StringencyIndex), max(sa$StringencyIndex), length.out = 100)) # values to predict for
X <- model.matrix(~ Year + StringencyIndex, data = newD) # exactly the model which was used has to be specified here
newD$pred <- (X %*% v)
predmatrix <- matrix(nrow = nrow(newD), ncol = nsim)
for (j in 1:nsim) predmatrix[, j] <- (X %*% bsim@fixef[j, ])
newD$lwr <- apply(predmatrix, 1, quantile, prob = 0.025)
newD$upr <- apply(predmatrix, 1, quantile, prob = 0.975)
newD$pred <- apply(predmatrix, 1, quantile, prob = 0.5)
newD$Country <- "Australia"
newD$Year <- NULL
l[[5]] <- newD
# Figure G_S
g_s = data.table(do.call(rbind,l))
g_s[, Country := factor(Country, levels = rev(c("Finland", "Poland", "Czechia", "Hungary", "Australia")))]
col3_ = c("#357EBDFF", "#D43F3AFF", "#46B8DAFF", "#5CB85CFF", "#EEA236FF", "#9632B8FF", "#9632B8FF")[7:1]
col3__ = col3_[3:7]
#p =
ggplot(g_s, aes(x = StringencyIndex, y = pred, col = Country)) +
geom_ribbon(aes(ymin = lwr, ymax = upr, fill = Country, color = NULL), alpha = .15) +
geom_jitter(aes(y = parks_percent_change_from_baseline, fill = Country), data = s, pch = 21, col = 'grey20', width = 0.7, height = 3, alpha = 0.5) +
geom_line(lwd = 1) +
labs(subtitle = "Mixed model per country predicitons", y = "Google Mobiligy\n[% change from baseline]", x = "Stringency Index") +
# scale_color_locuszoom()+
# scale_fill_locuszoom(guide = "none")
scale_x_continuous(breaks = round(seq(25, 75, by = 25), 1)) +
scale_y_continuous(breaks = round(seq(-100, 200, by = 50), 1)) +
#scale_y_continuous(breaks = round(seq(-100, 175, by = 25), 1)) +
scale_colour_manual(values = col3__, guide = guide_legend(reverse = TRUE, override.aes = list(size = 0)),
labels = paste("<span style='color:",
col3__,
"'>",
levels(sp$Country),
"</span>")
) +
scale_fill_manual(values = col3__, guide = "none") +
theme_bw() +
theme(
legend.text = element_markdown(size = 6),
#legend.position = "right",
legend.title = element_blank(),
# legend.spacing.y = unit(0.1, 'cm'),
legend.key.height = unit(0.5, "line"),
legend.key.size = unit(0, "line"),
legend.margin = margin(0, 0, 0, 0),
legend.box.margin=margin(-10,1,-10,-10),
# legend.position=c(0.5,1.6),
plot.title = element_text(color = "grey", size = 7),
plot.subtitle = element_text(color = "grey60", size = 6),
plot.margin = margin(b = 0.5, l = 0.5, t = 0.5, r = 0.5, unit = "pt"),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = ax_lines, size = 0.25),
axis.ticks = element_line(colour = ax_lines, size = 0.25),
# axis.text.x = element_text()
axis.ticks.length = unit(1, "pt"),
axis.text = element_text(, size = 6),
axis.title = element_text(size = 7)
)
ggsave(here::here("Outputs/Fig_G-S_rev_width_CustomLocusZoom_year-weekday.png"), width = 7, height = 6, unit = "cm", dpi = 600)Fig. 2 | Relationship betweeen Google Mobility and Stringency Index. Lines with shaded areas represent predicted relationships from country-specific mixed effect models controlled for the year and non-independence of data points by including weekday within the year as random intercept and Stringency Index as a random slope. Dots represent raw data, jittered to increase visibility. Colors indicate country. The predictions reveal generally negative and week relationship between Gooble Mobility (human activity) in parks and seveareness of governmental restrictions.
Table S_G_S | Google Mobility in relations to stringency index
ll = list()
s[, year_weekday := paste(Year, weekday)]
sf = s[Country == "Finland"]
fi <- lmer(scale(parks_percent_change_from_baseline) ~
scale(Year) +
scale(StringencyIndex) +
(scale(StringencyIndex) | year_weekday),
data = sf, REML = FALSE
)
ll[[1]] = m_out(name = "Table S_g-s - FI", dep = "Google Mobility", model = fi, nsim = 5000)
sp <- s[Country == "Poland"]
pl <- lmer(scale(parks_percent_change_from_baseline) ~
scale(Year) +
scale(StringencyIndex) +
(scale(StringencyIndex) | year_weekday),
data = sp, REML = FALSE
)
ll[[2]] = m_out(name = "Table S_g-s - PL", dep = "Google Mobility", model = pl, nsim = 5000)
sc = s[Country == "Czechia"]
cz <- lmer(scale(parks_percent_change_from_baseline) ~
scale(StringencyIndex) +
(scale(StringencyIndex) | weekday),
data = sc, REML = FALSE
)
ll[[3]] = m_out(name = "Table S_g-s - CZ", dep = "Google Mobility", model = cz, nsim = 5000)
sh <- s[Country == "Hungary"]
hu <- lmer(scale(parks_percent_change_from_baseline) ~
scale(Year) +
scale(StringencyIndex) +
(scale(StringencyIndex) | year_weekday),
data = sh, REML = FALSE
)
ll[[4]] = m_out(name = "Table S_g-s - HU", dep = "Google Mobility", model = hu, nsim = 5000)
sa <- s[Country == "Australia"]
au <- lmer(scale(parks_percent_change_from_baseline) ~
scale(Year) +
scale(StringencyIndex) +
(scale(StringencyIndex) | year_weekday),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = sa, REML = FALSE
)
ll[[5]] = m_out(name = "Table S_g-s - AU", dep = "Google Mobility", model = au, nsim = 5000)
out_g_s = data.table(do.call(rbind, ll))
out_g_s[is.na(out_g_s)] <- ""
out_g_s$R2_mar = out_g_s$R2_con = NULL
out_g_s[, effect := gsub("scale\\(Year\\)", "year", effect)]
out_g_s[, effect := gsub("scale\\(StringencyIndex\\)", "stringency index", effect)]
out_g_s[, effect := gsub("year_weekday", "weekday within year", effect)]
fwrite(file = here::here("Outputs/Table_S_G-S.csv"), out_g_s)
out_g_s %>%
kbl() %>%
kable_paper("hover", full_width = F)| model | response | error_structure | N | type | effect | estimate_r | lwr_r | upr_r |
|---|---|---|---|---|---|---|---|---|
| Table S_g-s - FI | Google Mobility | Gaussian | 322 | fixed | (Intercept) | -0.268 | -1.064 | 0.503 |
| fixed | year | 0.939 | 0.459 | 1.424 | ||||
| fixed | stringency index | -0.259 | -1.004 | 0.494 | ||||
| random % | weekday within year (Intercept) | 22% | 47% | 54% | ||||
| random % | weekday within year stringency index | 22% | 47% | 54% | ||||
| random % | Residual | 55% | -7% | 5% | ||||
| Table S_g-s - PL | Google Mobility | Gaussian | 329 | fixed | (Intercept) | 0.013 | -0.605 | 0.637 |
| fixed | year | 0.78 | 0.011 | 1.535 | ||||
| fixed | stringency index | 0.025 | -0.362 | 0.412 | ||||
| random % | weekday within year (Intercept) | 47% | 44% | 49% | ||||
| random % | weekday within year stringency index | 47% | 44% | 49% | ||||
| random % | Residual | 6% | 2% | 13% | ||||
| Table S_g-s - CZ | Google Mobility | Gaussian | 1054 | fixed | (Intercept) | 0.006 | -0.526 | 0.536 |
| fixed | stringency index | -0.219 | -0.635 | 0.189 | ||||
| random % | weekday (Intercept) | 23% | -184% | 40% | ||||
| random % | weekday stringency index | 23% | -184% | 40% | ||||
| random % | Residual | 54% | 21% | 469% | ||||
| Table S_g-s - HU | Google Mobility | Gaussian | 874 | fixed | (Intercept) | 0.255 | -0.272 | 0.737 |
| fixed | year | 0.007 | -0.457 | 0.466 | ||||
| fixed | stringency index | -1.264 | -1.946 | -0.566 | ||||
| random % | weekday within year (Intercept) | 48% | 2% | 49% | ||||
| random % | weekday within year stringency index | 48% | 2% | 49% | ||||
| random % | Residual | 4% | 2% | 95% | ||||
| Table S_g-s - AU | Google Mobility | Gaussian | 1065 | fixed | (Intercept) | 0.528 | -0.01 | 1.074 |
| fixed | year | 0.441 | 0.245 | 0.638 | ||||
| fixed | stringency index | -0.54 | -0.855 | -0.22 | ||||
| random % | weekday within year (Intercept) | -31% | 44% | 65% | ||||
| random % | weekday within year stringency index | -31% | 44% | 65% | ||||
| random % | Residual | 162% | -30% | 13% |
Continuous variables were z-transformed.
FID ~ Stringency
# predictions for fig
# full
mss <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(StringencyIndex) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = s, REML = FALSE,
control = lmerControl(
optimizer = "optimx", optCtrl = list(method = "nlminb")
)
)
est_mss <- est_out(mss, "ALL: (1|genus) + (1|Species) + (1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc)")
est_mss[, control_for_starting_distance := "yes"]
msx <- lmer(scale(log(FID)) ~
scale(Year) +
#scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(StringencyIndex) | Country) + (1| IDLocality) + (1 | sp_loc),
data = s, REML = FALSE,
control = lmerControl(
optimizer = "optimx", optCtrl = list(method = "nlminb")
)
)
est_msx <- est_out(msx, "ALL: (1|genus) + (1|Species) + (1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc)")
est_msx[, control_for_starting_distance := "no"]
# CZ - singular fits only due to genera estimated as zero (removing it changes no results)
css <- lmer(scale(log(FID)) ~
#scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1| Species) + (1 | sp_day_year) + (1| IDLocality) + (1|sp_loc),
#(1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = s[Country == "Czechia"], REML = FALSE
)
est_css <- est_out(css, "Czechia: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_css[, control_for_starting_distance := "yes"]
csx <- lmer(scale(log(FID)) ~
#scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1| Species) + (1 | sp_day_year) + (1| IDLocality) + (1|sp_loc),
#(1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = s[Country == "Czechia"], REML = FALSE
)
est_csx <- est_out(csx, "Czechia: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_csx[, control_for_starting_distance := "no"]
# FI
fss <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1| Species) + (1 | sp_day_year) + (1| IDLocality) + (1|sp_loc),
data = s[Country == "Finland"], REML = FALSE
)
est_fss <- est_out(fss, "Finland: (1|genus)+(1|Species)+(1|sp_day_year)+(scale(StringencyIndex)|IDLocality)+(1|sp_loc)")
est_fss[, control_for_starting_distance := "yes"]
fsx <- lmer(scale(log(FID)) ~
scale(Year) +
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1| Species) + (1 | sp_day_year) + (1| IDLocality) + (1|sp_loc),
data = s[Country == "Finland"], REML = FALSE
)
est_fsx <- est_out(fsx, "Finland: (1|genus)+(1|Species)+(1|sp_day_year)+(scale(StringencyIndex)|IDLocality)+(1|sp_loc)")
est_fsx[, control_for_starting_distance := "no"]
# HU - singular fits only due to sp_loc estimated as zero (removing it changes no results)
hss <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = s[Country == "Hungary"], REML = FALSE
)
est_hss <- est_out(hss, "Hungary: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_hss[, control_for_starting_distance := "yes"]
hsx <- lmer(scale(log(FID)) ~
scale(Year) +
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = s[Country == "Hungary"], REML = FALSE
)
est_hsx <- est_out(hsx, "Hungary: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_hsx[, control_for_starting_distance := "no"]
# AU - singular fits only due to Year and random slope estimated as zero (removing those changes no results)
ass <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = s[Country == "Australia"], REML = FALSE
)
est_ass <- est_out(ass, "Australia: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_ass[, control_for_starting_distance := "yes"]
asx <- lmer(scale(log(FID)) ~
scale(Year) +
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = s[Country == "Australia"], , REML = FALSE,
control = lmerControl(
optimizer ='optimx', optCtrl=list(method='nlminb'))
)
est_asx <- est_out(asx, "Australia: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_asx[, control_for_starting_distance := "no"]
# PL
pss <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1 | Species) + (1 | sp_day_year),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = s[Country == "Poland"], REML = FALSE
)
est_pss <- est_out(pss, "Poland: (1|genus)+(1|Species)+(1|sp_day_year)")
est_pss[, control_for_starting_distance := "yes"]
psx <- lmer(scale(log(FID)) ~
scale(Year) +
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | genus) + (1 | Species) + (1 | sp_day_year),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = s[Country == "Poland"], REML = FALSE
)
est_psx <- est_out(psx, "Poland: (1|genus)+(1|Species)+(1|sp_day_year)")
est_psx[, control_for_starting_distance := "no"]
# combine
est_mss[, Country := 'All\n(mixed model)']
est_msx[, Country := "All\n(mixed model)"]
est_ass[, Country := "Australia"]
est_asx[, Country := "Australia"]
est_css[, Country := "Czechia"]
est_csx[, Country := "Czechia"]
est_hss[, Country := "Hungary"]
est_hsx[, Country := "Hungary"]
est_pss[, Country := "Poland"]
est_psx[, Country := "Poland"]
est_fss[, Country := "Finland"]
est_fsx[, Country := "Finland"]
os = rbind(est_mss, est_msx,
est_ass, est_asx,
est_css, est_csx,
est_hss, est_hsx,
est_pss, est_psx,
est_fss, est_fsx)
save(os, file = here::here('Data/dat_est_Stringency_rev.Rdata'))
# estimates for table
mss_out <- m_out(name = "Table S_FID_s - full a", dep = "Escape distance", model = mss, nsim = 5000)
msx_out <- m_out(name = "Table S_FID_s - full b", dep = "Escape distance", model = msx, nsim = 5000)
css_out <- m_out(name = "Table v - CZ a", dep = "Escape distance", model = css, nsim = 5000)
csx_out <- m_out(name = "Table S_FID_s - CZ b", dep = "Escape distance", model = csx, nsim = 5000)
fss_out <- m_out(name = "Table S_FID_s - FI a", dep = "Escape distance", model = fss, nsim = 5000)
fsx_out <- m_out(name = "Table S_FID_s - FI b", dep = "Escape distance", model = fsx, nsim = 5000)
hss_out <- m_out(name = "Table S_FID_s - HU a", dep = "Escape distance", model = hss, nsim = 5000)
hsx_out <- m_out(name = "Table S_FID_s - HU b", dep = "Escape distance", model = hsx, nsim = 5000)
ass_out <- m_out(name = "Table S_FID_s - AU a", dep = "Escape distance", model = ass, nsim = 5000)
asx_out <- m_out(name = "Table S_FID_s - AU b", dep = "Escape distancey", model = asx, nsim = 5000)
pss_out <- m_out(name = "Table S_FID_s - PL a", dep = "Escape distance", model = pss, nsim = 5000)
psx_out <- m_out(name = "Table S_FID_s - PL b", dep = "Escape distancey", model = psx, nsim = 5000)
out_FID_s <- rbind(mss_out, msx_out, fss_out, fsx_out, pss_out, psx_out, css_out, csx_out, hss_out, hsx_out, ass_out, asx_out, fill = TRUE)
out_FID_s[is.na(out_FID_s)] <- ""
out_FID_s$R2_mar = out_FID_s$R2_con = NULL
out_FID_s[, effect := gsub("scale\\(StringencyIndex\\)", "Stringency index", effect)]
out_FID_s[, effect := gsub("scale\\(Year\\)", "year", effect)]
out_FID_s[, effect := gsub("scale\\(log\\(SD\\)", "starting distance (ln)", effect)]
out_FID_s[, effect := gsub("scale\\(Temp\\)", "temperaturre", effect)]
out_FID_s[, effect := gsub("scale\\(log\\(FlockSize\\)\\)", "flock size", effect)]
out_FID_s[, effect := gsub("scale\\(log\\(BodyMass\\)\\)", "body mass", effect)]
out_FID_s[, effect := gsub("scale\\(sin\\(rad\\)\\)", "sine of radians", effect)]
out_FID_s[, effect := gsub("scale\\(cos\\(rad\\)\\)", "cosine of radians", effect)]
out_FID_s[, effect := gsub("Species", "species", effect)]
out_FID_s[, effect := gsub("sp_day_year", "species within day & year", effect)]
out_FID_s[, effect := gsub("IDLocality", "site", effect)]
out_FID_s[, effect := gsub("sp_loc", "species within site", effect)]
fwrite(file = here::here("Outputs/Table_S_FID_s_c.csv"), out_FID_s)load(here::here("Data/dat_est_Stringency_rev.Rdata"))
os[predictor %in% c("scale(StringencyIndex)"), predictor := "Stringency Index"]
oso <- os[predictor %in% c("Stringency Index")]
oso[, N:=as.numeric(sub('.*N = ', '', model))]
# add meta-analytical mean
oso_s = oso[control_for_starting_distance == 'yes']
met = summary(meta.summaries(d = oso_s$estimate, se = oso_s$sd, method = "fixed", weights = oso_s$N))$summci
oso_met = data.table(predictor = "Period", estimate = met[2], lwr = met[1], upr = met[3], sd = NA, model = NA, control_for_starting_distance = "yes", Country = "Combined\n(metanalytical)", N = NA)
oso_sx = oso[control_for_starting_distance == "no"]
metx = summary(meta.summaries(d = oso_sx$estimate, se = oso_sx$sd, method = "fixed", weights = oso_sx$N))$summci
oso_metx = data.table(predictor = "Period", estimate = metx[2], lwr = metx[1], upr = metx[3], sd = NA, model = NA, control_for_starting_distance = "no", Country = "Combined\n(metanalytical)", N = NA)
oso = rbind(oso, oso_met, oso_metx)
oso[, Country := factor(Country, levels = rev(c("Finland", "Poland", "Czechia", "Hungary", "Australia", "Combined\n(metanalytical)", "All\n(mixed model)")))]
width_ <- .5 # spacing between error bars
#col_ <- c(brewer.pal(n = 12, name = "Paired"), "grey30", "grey80")
#Tol_bright <- c("#EE6677", "#228833", "#4477AA", "#CCBB44", "#66CCEE", "#AA3377", "#BBBBBB")
#Tol_muted <- c("#88CCEE", "#44AA99", "#117733", "#332288", "#DDCC77", "#999933", "#CC6677", "#882255", "#AA4499", "#DDDDDD")
#Tol_light <- c("#BBCC33", "#AAAA00", "#77AADD", "#EE8866", "#EEDD88", "#FFAABB", "#99DDFF", "#44BB99", "#DDDDDD")
# From Color Universal Design (CUD): https://jfly.uni-koeln.de/color/
#Okabe_Ito <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#000000")
#col_ = Okabe_Ito[7:1]
# JAMA and LocusZoom modified order
#col_ = c("#374E55FF", "#374E55FF", "#DF8F44FF", "#79AF97FF", "#00A1D5FF", "#B24745FF", "#80796BFF") #"#6A6599FF",
#col_ <- c("#357EBDFF", "#9632B8FF", "#46B8DAFF", "#5CB85CFF", "#EEA236FF", "#D43F3AFF", "#D43F3AFF")[7:1] # "#D43F3AFF", "#B8B8B8FF"
col_ = c("#357EBDFF", "#D43F3AFF", "#46B8DAFF", "#5CB85CFF", "#EEA236FF", "#9632B8FF", "#9632B8FF")[7:1] # "#D43F3AFF", "#B8B8B8FF"
#show_col(col_)
#p =
ggplot(oso, aes(x = estimate, y = Country, col = Country, shape = control_for_starting_distance)) +
geom_vline(xintercept = 0, color = "grey", linetype = "dotted") +
geom_errorbarh(aes(xmin = lwr, xmax = upr), height = 0, position = ggstance::position_dodgev(width_)) +
# geom_point(position = ggstance::position_dodgev(.6)) +
geom_point(position = position_dodge(width = width_), bg = "white", size = 1.1) +
# scale_color_viridis(discrete=TRUE, begin=0, end = 0.5) +
# scale_fill_viridis(discrete=TRUE, begin=0, end = 0.5) +
# geom_text( aes(x = n_pos,label = N), vjust = 0, size = 1.75, position = ggstance::position_dodgev(width_))+ # 3 positions for 3 bars
# annotate("text", x=log10(3), y=85, label= "Used", col = "grey30", size = 2.5)+
scale_shape_manual(name = "Controlled for\nstarting distance", guide = guide_legend(reverse = TRUE), values = c(21, 19)) +
#scale_color_jama(guide = "none")+ #, palette = 'light'
scale_color_manual(guide = "none", values = col_) + #guide_legend(reverse = TRUE)
scale_x_continuous(breaks = round(seq(-0.3, 0.4, by = 0.1), 1)) +
ylab("") +
xlab("Standardized effect size of Stringency Index\n[on flight initiation distance]") +
# coord_cartesian(xlim = c(-.15, .15)) +
# scale_x_continuous(breaks = round(seq(-.15, .15, by = 0.05),2)) +
theme_bw() +
theme(
legend.position = "right",
legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
# legend.spacing.y = unit(0.1, 'cm'),
legend.key.height = unit(0.5, "line"),
legend.margin = margin(0, 0, 0, 0),
# legend.position=c(0.5,1.6),
plot.title = element_text(color = "grey", size = 7),
plot.margin = margin(b = 0.5, l = 0.5, t = 0.5, r = 0.5, unit = "pt"),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = ax_lines, size = 0.25),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.x = element_line(colour = ax_lines, size = 0.25),
# axis.text.x = element_text()
axis.ticks.length = unit(1, "pt"),
axis.text.x = element_text(, size = 6),
axis.text.y = element_text(colour = "black", size = 7),
axis.title = element_text(size = 7)
)
ggsave(here::here("Outputs/Fig_Stringency_rev_width_CustomLocusZoom_v2.png"), width = 8, height = 6, unit = "cm", dpi = 600)Fig. S_stringency | The effect size with 95%CIs of Stringency Index on flight initiation distance (ln-transformed). The general model on all data was controlled for year, starting distance (ln-transformed; filled circles) or not (empty circles), flock size (ln-transformed), temperature (also a proxy for a day within the breeding season: r Pearson = 0.48; Fig. S1) and time of day. To account for circular properties of time, time was transformed into radians (2 × time × π/24) and fitted as sine and cosine of radians (Bulla et al. 2016). All continuous variables were standardised by subtracting the mean and dividing by the standard deviation. The multicollinearity was small as correlations between predictors were weak (r<XX, Fig. S1). To account for the non-independence of data points (Schielzeth & Forstmeier 2009; Barr et al. 2013), we fitted random intercepts of genus, species, species at a given day and year, site, and species within a site and in the full model Strigency also random slope of Stringency within genus and country. In addition, we attempted to fit Stringency as random slope within genus for country-specific modl, but it explained little variance and estimates remained the same. Note that in the full model, fitting Stringency as random slope at other random intercepts produces similar results (see Fig Sxx). We used this approach with a full dataset with all observations (n = xx), as well as with conservative datasets, one with at least five observations per species , the other with at least 10 observations per species. Albeit random structure of the full model accounts for the potential country specific effect (country accounted for xx% of variance, Table xx), depicted are also estimates from country specific models, with same predictors and random structure as the full model (excluding the random slopes, and random intercept of country and in case of Poland also site, as specific sites were not noted in Poland). Using meta.summaries function from rmeta R-package we used the country estimates, their standard deviation, and sample size per country to estimate the meta-analytical mean, which reflects the results based on the full mixed effect model. Importantly, the models test for within year changes in esccape distancces due to human changes presence, i.e. week to week changes in Stringency Index. In other words, the models investigate whether birds flexibly adjust their fear response, a test different to the one intended in this study. Albeit the response of humans to governmental restrictions were likely country specific and presence of humans in parks might have increased in some countries, decreased in others or did not change, the escape distances do not strongly reflect such changes. In other words, the birds are either inflexible or the change in human behavior due to shutdowns was not strong enought, which might have been the case - see Fig. ZZ - decide what to show in this figure (I think 2020-2022 changes in Google Mobility for each country including the fits and refering to supplementary figure). Note however that human presence consistently declined with increased restrictions across countries, albeit the relationships are weak (Fig. 2).
Table FID_s | Escape distance in relations to stringency index
out_FID_s %>%
kbl() %>%
kable_paper("hover", full_width = F)| model | response | error_structure | N | type | effect | estimate_r | lwr_r | upr_r |
|---|---|---|---|---|---|---|---|---|
| Table S_FID_s - full a | Escape distance | Gaussian | 3676 | fixed | (Intercept) | -0.076 | -0.334 | 0.187 |
| fixed | year | 0.02 | -0.027 | 0.066 | ||||
| fixed | starting distance (ln)) | 0.491 | 0.46 | 0.523 | ||||
| fixed | flock size | -0.003 | -0.03 | 0.024 | ||||
| fixed | body mass | 0.023 | -0.047 | 0.092 | ||||
| fixed | sine of radians | -0.02 | -0.061 | 0.023 | ||||
| fixed | cosine of radians | -0.005 | -0.04 | 0.03 | ||||
| fixed | temperaturre | -0.007 | -0.047 | 0.035 | ||||
| fixed | Stringency index | 0.015 | -0.157 | 0.183 | ||||
| random % | species within day & year (Intercept) | 5% | 4% | 7% | ||||
| random % | species within site (Intercept) | 5% | 4% | 6% | ||||
| random % | species (Intercept) | 8% | 6% | 8% | ||||
| random % | site (Intercept) | 8% | 7% | 9% | ||||
| random % | genus (Intercept) | 4% | 4% | 4% | ||||
| random % | Country (Intercept) | 1% | -14% | 14% | ||||
| random % | Country Stringency index | 1% | -14% | 14% | ||||
| random % | Residual | 68% | 47% | 93% | ||||
| Table S_FID_s - full b | Escape distance | Gaussian | 3676 | fixed | (Intercept) | -0.113 | -0.458 | 0.241 |
| fixed | year | 0.085 | 0.028 | 0.141 | ||||
| fixed | flock size | 0.01 | -0.019 | 0.04 | ||||
| fixed | body mass | 0.136 | 0.049 | 0.223 | ||||
| fixed | sine of radians | 0.045 | -0.002 | 0.093 | ||||
| fixed | cosine of radians | 0.048 | 0.008 | 0.088 | ||||
| fixed | temperaturre | 0.008 | -0.039 | 0.055 | ||||
| fixed | Stringency index | 0.01 | -0.216 | 0.234 | ||||
| random % | species within day & year (Intercept) | 6% | 4% | 10% | ||||
| random % | species within site (Intercept) | 5% | 4% | 9% | ||||
| random % | species (Intercept) | 10% | 7% | 14% | ||||
| random % | site (Intercept) | 11% | 8% | 15% | ||||
| random % | genus (Intercept) | 6% | 5% | 8% | ||||
| random % | Country (Intercept) | 1% | -26% | 18% | ||||
| random % | Country Stringency index | 1% | -26% | 18% | ||||
| random % | Residual | 58% | 36% | 97% | ||||
| Table S_FID_s - FI a | Escape distance | Gaussian | 354 | fixed | (Intercept) | 0.033 | -0.179 | 0.243 |
| fixed | year | -0.105 | -0.337 | 0.121 | ||||
| fixed | starting distance (ln)) | 0.187 | 0.086 | 0.291 | ||||
| fixed | flock size | -0.111 | -0.21 | -0.008 | ||||
| fixed | body mass | 0.177 | 0.012 | 0.34 | ||||
| fixed | sine of radians | 0.06 | -0.062 | 0.184 | ||||
| fixed | cosine of radians | 0.012 | -0.109 | 0.135 | ||||
| fixed | temperaturre | -0.084 | -0.197 | 0.028 | ||||
| fixed | Stringency index | 0.156 | -0.074 | 0.381 | ||||
| random % | species within site (Intercept) | 12% | 12% | 13% | ||||
| random % | species within day & year (Intercept) | 3% | 3% | 3% | ||||
| random % | site (Intercept) | 12% | 10% | 15% | ||||
| random % | species (Intercept) | 0% | 0% | 1% | ||||
| random % | genus (Intercept) | 3% | 2% | 5% | ||||
| random % | Residual | 68% | 64% | 73% | ||||
| Table S_FID_s - FI b | Escape distance | Gaussian | 354 | fixed | (Intercept) | -0.012 | -0.231 | 0.209 |
| fixed | year | -0.067 | -0.309 | 0.167 | ||||
| fixed | flock size | -0.107 | -0.206 | -0.003 | ||||
| fixed | body mass | 0.249 | 0.079 | 0.414 | ||||
| fixed | sine of radians | 0.054 | -0.074 | 0.179 | ||||
| fixed | cosine of radians | 0.005 | -0.117 | 0.124 | ||||
| fixed | temperaturre | -0.086 | -0.2 | 0.025 | ||||
| fixed | Stringency index | 0.137 | -0.092 | 0.374 | ||||
| random % | species within site (Intercept) | 14% | 14% | 15% | ||||
| random % | species within day & year (Intercept) | 4% | 3% | 4% | ||||
| random % | site (Intercept) | 14% | 11% | 16% | ||||
| random % | species (Intercept) | 3% | 2% | 5% | ||||
| random % | genus (Intercept) | 1% | 1% | 2% | ||||
| random % | Residual | 64% | 59% | 69% | ||||
| Table S_FID_s - PL a | Escape distance | Gaussian | 329 | fixed | (Intercept) | 0.002 | -0.128 | 0.136 |
| fixed | year | -0.161 | -0.394 | 0.073 | ||||
| fixed | starting distance (ln)) | 0.532 | 0.447 | 0.617 | ||||
| fixed | flock size | -0.024 | -0.104 | 0.06 | ||||
| fixed | body mass | -0.098 | -0.205 | 0.009 | ||||
| fixed | sine of radians | 0.102 | -0.16 | 0.36 | ||||
| fixed | cosine of radians | -0.157 | -0.416 | 0.112 | ||||
| fixed | temperaturre | -0.171 | -0.276 | -0.065 | ||||
| fixed | Stringency index | 0.027 | -0.207 | 0.262 | ||||
| random % | species within day & year (Intercept) | 13% | 12% | 14% | ||||
| random % | species (Intercept) | 8% | 6% | 10% | ||||
| random % | genus (Intercept) | 0% | 0% | 0% | ||||
| random % | Residual | 79% | 77% | 82% | ||||
| Table S_FID_s - PL b | Escape distancey | Gaussian | 329 | fixed | (Intercept) | 0.001 | -0.199 | 0.203 |
| fixed | year | -0.363 | -0.634 | -0.101 | ||||
| fixed | flock size | 0.06 | -0.037 | 0.16 | ||||
| fixed | body mass | -0.028 | -0.181 | 0.121 | ||||
| fixed | sine of radians | -0.006 | -0.304 | 0.305 | ||||
| fixed | cosine of radians | -0.043 | -0.344 | 0.276 | ||||
| fixed | temperaturre | -0.162 | -0.288 | -0.037 | ||||
| fixed | Stringency index | -0.027 | -0.298 | 0.24 | ||||
| random % | species within day & year (Intercept) | 7% | 7% | 7% | ||||
| random % | species (Intercept) | 22% | 17% | 26% | ||||
| random % | genus (Intercept) | 0% | 0% | 0% | ||||
| random % | Residual | 72% | 67% | 77% | ||||
| Table v - CZ a | Escape distance | Gaussian | 1054 | fixed | (Intercept) | 0.223 | 0.025 | 0.437 |
| fixed | starting distance (ln)) | 0.354 | 0.295 | 0.413 | ||||
| fixed | flock size | 0.004 | -0.045 | 0.056 | ||||
| fixed | body mass | 0.181 | 0.026 | 0.341 | ||||
| fixed | sine of radians | 0.062 | -0.013 | 0.136 | ||||
| fixed | cosine of radians | 0.007 | -0.063 | 0.077 | ||||
| fixed | temperaturre | 0.011 | -0.076 | 0.098 | ||||
| fixed | Stringency index | -0.062 | -0.181 | 0.058 | ||||
| random % | species within site (Intercept) | 4% | 4% | 4% | ||||
| random % | species within day & year (Intercept) | 2% | 2% | 2% | ||||
| random % | species (Intercept) | 15% | 12% | 17% | ||||
| random % | genus (Intercept) | 14% | 10% | 18% | ||||
| random % | site (Intercept) | 5% | 3% | 6% | ||||
| random % | Residual | 61% | 54% | 68% | ||||
| Table S_FID_s - CZ b | Escape distance | Gaussian | 1054 | fixed | (Intercept) | 0.229 | 0.02 | 0.433 |
| fixed | starting distance (ln)) | 0.355 | 0.297 | 0.413 | ||||
| fixed | flock size | 0.004 | -0.045 | 0.056 | ||||
| fixed | body mass | 0.182 | 0.028 | 0.342 | ||||
| fixed | sine of radians | 0.061 | -0.012 | 0.137 | ||||
| fixed | cosine of radians | 0.006 | -0.064 | 0.078 | ||||
| fixed | temperaturre | 0.01 | -0.08 | 0.098 | ||||
| fixed | Stringency index | -0.061 | -0.179 | 0.056 | ||||
| random % | species within site (Intercept) | 4% | 4% | 4% | ||||
| random % | species within day & year (Intercept) | 2% | 2% | 2% | ||||
| random % | species (Intercept) | 15% | 12% | 17% | ||||
| random % | genus (Intercept) | 14% | 10% | 18% | ||||
| random % | site (Intercept) | 5% | 3% | 6% | ||||
| random % | Residual | 61% | 54% | 68% | ||||
| Table S_FID_s - HU a | Escape distance | Gaussian | 874 | fixed | (Intercept) | 0.069 | -0.212 | 0.342 |
| fixed | year | 0.193 | 0.089 | 0.301 | ||||
| fixed | starting distance (ln)) | 0.507 | 0.447 | 0.569 | ||||
| fixed | flock size | 0.002 | -0.052 | 0.054 | ||||
| fixed | body mass | -0.001 | -0.14 | 0.141 | ||||
| fixed | sine of radians | -0.124 | -0.19 | -0.057 | ||||
| fixed | cosine of radians | 0.033 | -0.039 | 0.105 | ||||
| fixed | temperaturre | -0.008 | -0.103 | 0.09 | ||||
| fixed | Stringency index | 0.045 | -0.096 | 0.183 | ||||
| random % | species within day & year (Intercept) | 3% | 3% | 3% | ||||
| random % | species within site (Intercept) | 0% | 0% | 0% | ||||
| random % | species (Intercept) | 1% | 1% | 1% | ||||
| random % | genus (Intercept) | 11% | 7% | 15% | ||||
| random % | site (Intercept) | 10% | 7% | 13% | ||||
| random % | Residual | 74% | 67% | 82% | ||||
| Table S_FID_s - HU b | Escape distance | Gaussian | 874 | fixed | (Intercept) | 0.055 | -0.402 | 0.521 |
| fixed | year | 0.236 | 0.107 | 0.365 | ||||
| fixed | flock size | 0.004 | -0.056 | 0.063 | ||||
| fixed | body mass | 0.295 | 0.066 | 0.521 | ||||
| fixed | sine of radians | -0.14 | -0.221 | -0.064 | ||||
| fixed | cosine of radians | 0.034 | -0.051 | 0.115 | ||||
| fixed | temperaturre | -0.031 | -0.147 | 0.08 | ||||
| fixed | Stringency index | 0.003 | -0.159 | 0.161 | ||||
| random % | species within day & year (Intercept) | 5% | 5% | 6% | ||||
| random % | species within site (Intercept) | 0% | 0% | 0% | ||||
| random % | species (Intercept) | 1% | 1% | 1% | ||||
| random % | genus (Intercept) | 25% | 18% | 31% | ||||
| random % | site (Intercept) | 18% | 16% | 19% | ||||
| random % | Residual | 51% | 43% | 60% | ||||
| Table S_FID_s - AU a | Escape distance | Gaussian | 1065 | fixed | (Intercept) | -0.049 | -0.189 | 0.09 |
| fixed | year | 0.036 | -0.03 | 0.1 | ||||
| fixed | starting distance (ln)) | 0.496 | 0.444 | 0.549 | ||||
| fixed | flock size | 0.024 | -0.024 | 0.072 | ||||
| fixed | body mass | -0.052 | -0.146 | 0.044 | ||||
| fixed | sine of radians | -0.037 | -0.109 | 0.035 | ||||
| fixed | cosine of radians | -0.052 | -0.119 | 0.016 | ||||
| fixed | temperaturre | 0.033 | -0.028 | 0.094 | ||||
| fixed | Stringency index | 0.058 | -0.006 | 0.123 | ||||
| random % | species within day & year (Intercept) | 6% | 6% | 6% | ||||
| random % | species within site (Intercept) | 12% | 12% | 12% | ||||
| random % | species (Intercept) | 11% | 9% | 13% | ||||
| random % | genus (Intercept) | 2% | 2% | 3% | ||||
| random % | site (Intercept) | 7% | 5% | 9% | ||||
| random % | Residual | 61% | 57% | 66% | ||||
| Table S_FID_s - AU b | Escape distancey | Gaussian | 1065 | fixed | (Intercept) | -0.101 | -0.287 | 0.087 |
| fixed | year | 0.104 | 0.029 | 0.176 | ||||
| fixed | flock size | 0.055 | 0.001 | 0.111 | ||||
| fixed | body mass | 0.062 | -0.063 | 0.184 | ||||
| fixed | sine of radians | -0.011 | -0.095 | 0.072 | ||||
| fixed | cosine of radians | -0.019 | -0.095 | 0.059 | ||||
| fixed | temperaturre | 0.041 | -0.029 | 0.11 | ||||
| fixed | Stringency index | 0.091 | 0.015 | 0.164 | ||||
| random % | species within day & year (Intercept) | 7% | 7% | 8% | ||||
| random % | species within site (Intercept) | 11% | 11% | 11% | ||||
| random % | species (Intercept) | 13% | 11% | 15% | ||||
| random % | genus (Intercept) | 7% | 6% | 8% | ||||
| random % | site (Intercept) | 8% | 6% | 10% | ||||
| random % | Residual | 54% | 49% | 58% |
Continuous variables were z-transformed.
FID ~ Google Mobiliity
# predictions for fig
# full
mgs <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(scale(parks_percent_change_from_baseline)| genus) + (1 | Species) + (1 | sp_day_year) +
(scale(parks_percent_change_from_baseline)| Country) + (1 | IDLocality) + (1 | sp_loc),
data = ss, REML = FALSE,
control = lmerControl(
optimizer = "optimx", optCtrl = list(method = "nlminb")
)
)
est_mgs <- est_out(mgs, "ALL: (scale(Google)|genus) + (1|Species) + (1|sp_day_year) + (scale(Google)|Country) + (1|IDLocality) +(1|sp_loc)")
est_mgs[, control_for_starting_distance := "yes"]
mgx <- lmer(scale(log(FID)) ~
scale(Year) +
#scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
scale(parks_percent_change_from_baseline) +
(scale(parks_percent_change_from_baseline)| genus) + (1 | Species) + (1 | sp_day_year) +
(scale(parks_percent_change_from_baseline) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = ss, REML = FALSE,
control = lmerControl(
optimizer = "optimx", optCtrl = list(method = "nlminb")
)
)
est_mgx <- est_out(mgx, "ALL: (scale(Google)|genus) + (1|Species) + (1|sp_day_year) + (scale(Google)|Country) + (1|IDLocality) +(1|sp_loc)")
est_mgx[, control_for_starting_distance := "no"]
# CZ - singular fits only due to genera estimated as zero (removing it changes no results)
cgs <- lmer(scale(log(FID)) ~
#scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1| Species) + (1 | sp_day_year) + (1| IDLocality) + (1|sp_loc),
#(1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = ss[Country == "Czechia"], REML = FALSE
)
est_cgs <- est_out(cgs, "Czechia: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_cgs[, control_for_starting_distance := "yes"]
cgx <- lmer(scale(log(FID)) ~
#scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1| Species) + (1 | sp_day_year) + (1| IDLocality) + (1|sp_loc),
#(1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = ss[Country == "Czechia"], REML = FALSE
)
est_cgx <- est_out(cgx, "Czechia: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_cgx[, control_for_starting_distance := "no"]
# FI
fgs <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1| Species) + (1 | sp_day_year) + (1| IDLocality) + (1|sp_loc),
data = ss[Country == "Finland"], REML = FALSE
)
est_fgs <- est_out(fgs, "Finland: (1|genus)+(1|Species)+(1|sp_day_year)+(scale(StringencyIndex)|IDLocality)+(1|sp_loc)")
est_fgs[, control_for_starting_distance := "yes"]
fgx <- lmer(scale(log(FID)) ~
scale(Year) +
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1| Species) + (1 | sp_day_year) + (1| IDLocality) + (1|sp_loc),
data = ss[Country == "Finland"], REML = FALSE
)
est_fgx <- est_out(fgx, "Finland: (1|genus)+(1|Species)+(1|sp_day_year)+(scale(StringencyIndex)|IDLocality)+(1|sp_loc)")
est_fgx[, control_for_starting_distance := "no"]
# HU - singular fits only due to sp_loc estimated as zero (removing it changes no results)
hgs <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = ss[Country == "Hungary"], REML = FALSE
)
est_hgs <- est_out(hgs, "Hungary: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_hgs[, control_for_starting_distance := "yes"]
hgx <- lmer(scale(log(FID)) ~
scale(Year) +
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = ss[Country == "Hungary"], REML = FALSE
)
est_hgx <- est_out(hgx, "Hungary: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_hgx[, control_for_starting_distance := "no"]
# AU - singular fits only due to Year and random slope estimated as zero (removing those changes no results)
ags <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = ss[Country == "Australia"], REML = FALSE
)
est_ags <- est_out(ags, "Australia: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_ags[, control_for_starting_distance := "yes"]
agx <- lmer(scale(log(FID)) ~
scale(Year) +
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality) + (1 | sp_loc),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = ss[Country == "Australia"], , REML = FALSE,
control = lmerControl(
optimizer ='optimx', optCtrl=list(method='nlminb'))
)
est_agx <- est_out(agx, "Australia: (1|genus)+(1|Species)+(1|sp_day_year)+(1|IDLocality)+(1|sp_loc)")
est_agx[, control_for_starting_distance := "no"]
# PL
pgs <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1 | Species) + (1 | sp_day_year),
# (1 | Year) + (1 | weekday) + (1|genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = ss[Country == "Poland"], REML = FALSE
)
est_pgs <- est_out(pgs, "Poland: (1|genus)+(1|Species)+(1|sp_day_year)")
est_pgs[, control_for_starting_distance := "yes"]
pgx <- lmer(scale(log(FID)) ~
scale(Year) +
# scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | genus) + (1 | Species)+ (1 | sp_day_year),
# (1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (1 | IDLocality),
data = ss[Country == "Poland"], REML = FALSE
)
est_pgx <- est_out(pgx, "Poland: (1|genus)+(1|Species)+(1|sp_day_year)")
est_pgx[, control_for_starting_distance := "no"]
# combine
est_mgs[, Country := 'All\n(mixed model)']
est_mgx[, Country := "All\n(mixed model)"]
est_ags[, Country := "Australia"]
est_agx[, Country := "Australia"]
est_cgs[, Country := "Czechia"]
est_cgx[, Country := "Czechia"]
est_hgs[, Country := "Hungary"]
est_hgx[, Country := "Hungary"]
est_pgs[, Country := "Poland"]
est_pgx[, Country := "Poland"]
est_fgs[, Country := "Finland"]
est_fgx[, Country := "Finland"]
og = rbind(est_mgs, est_mgx,
est_ags, est_agx,
est_cgs, est_cgx,
est_hgs, est_hgx,
est_pgs, est_pgx,
est_fgs, est_fgx)
save(og, file = here::here('Data/dat_est_Google_rev.Rdata'))
# estimatees for table
mgs_out <- m_out(name = "Table S_FID_g - full a", dep = "Escape distance", model = mgs, nsim = 5000)
mgx_out <- m_out(name = "Table S_FID_g - full b", dep = "Escape distance", model = mgx, nsim = 5000)
cgs_out <- m_out(name = "Table S_FID_g - CZ a", dep = "Escape distance", model = cgs, nsim = 5000)
cgx_out <- m_out(name = "Table S_FID_g - CZ b", dep = "Escape distance", model = cgx, nsim = 5000)
fgs_out <- m_out(name = "Table S_FID_g - FI a", dep = "Escape distance", model = fgs, nsim = 5000)
fgx_out <- m_out(name = "Table S_FID_g - FI b", dep = "Escape distance", model = fgx, nsim = 5000)
hgs_out <- m_out(name = "Table S_FID_g - HU a", dep = "Escape distance", model = hgs, nsim = 5000)
hgx_out <- m_out(name = "Table S_FID_g - HU b", dep = "Escape distance", model = hgx, nsim = 5000)
ags_out <- m_out(name = "Table S_FID_g - AU a", dep = "Escape distance", model = ags, nsim = 5000)
agx_out <- m_out(name = "Table S_FID_g - AU b", dep = "Escape distancey", model = agx, nsim = 5000)
pgs_out <- m_out(name = "Table S_FID_g - PL a", dep = "Escape distance", model = pgs, nsim = 5000)
pgx_out <- m_out(name = "Table S_FID_g - PL b", dep = "Escape distancey", model = pgx, nsim = 5000)
out_FID_g <- rbind(mgs_out, mgx_out, fgs_out, fgx_out, pgs_out, pgx_out, cgs_out, cgx_out, hgs_out, hgx_out, ags_out, agx_out, fill = TRUE)
out_FID_g[is.na(out_FID_g)] <- ""
out_FID_g[, effect := gsub("scale\\(parks_percent_change_from_baseline\\)", "Google Mobility", effect)]
out_FID_g[, effect := gsub("scale\\(Year\\)", "year", effect)]
out_FID_g[, effect := gsub("scale\\(log\\(SD\\)", "starting distance (ln)", effect)]
out_FID_g[, effect := gsub("scale\\(Temp\\)", "temperaturre", effect)]
out_FID_g[, effect := gsub("scale\\(log\\(FlockSize\\)\\)", "flock size (ln)", effect)]
out_FID_g[, effect := gsub("scale\\(log\\(BodyMass\\)\\)", "body mass (ln)", effect)]
out_FID_g[, effect := gsub("scale\\(sin\\(rad\\)\\)", "sine of radians", effect)]
out_FID_g[, effect := gsub("scale\\(cos\\(rad\\)\\)", "cosine of radians", effect)]
out_FID_g[, effect := gsub("Species", "species", effect)]
out_FID_g[, effect := gsub("sp_day_year", "species within day & year", effect)]
out_FID_g[, effect := gsub("IDLocality", "site", effect)]
out_FID_g[, effect := gsub("sp_loc", "species within site", effect)]
fwrite(file = here::here("Outputs/Table_S_FID_g_c.csv"), out_FID_g)load(here::here("Data/dat_est_Google_rev.Rdata"))
og[predictor %in% c("scale(parks_percent_change_from_baseline)"), predictor := "Google Mobility"]
ogo <- og[predictor %in% c("Google Mobility")]
ogo[, N:=as.numeric(sub('.*N = ', '', model))]
# add meta-analytical mean
ogo_s = ogo[control_for_starting_distance == 'yes']
met = summary(meta.summaries(d = ogo_s$estimate, se = ogo_s$sd, method = "fixed", weights = ogo_s$N))$summci
ogo_met = data.table(predictor = "Period", estimate = met[2], lwr = met[1], upr = met[3], sd = NA, model = NA, control_for_starting_distance = "yes", Country = "Combined\n(metanalytical)", N = NA)
ogo_sx = ogo[control_for_starting_distance == "no"]
metx = summary(meta.summaries(d = ogo_sx$estimate, se = ogo_sx$sd, method = "fixed", weights = ogo_sx$N))$summci
ogo_metx = data.table(predictor = "Period", estimate = metx[2], lwr = metx[1], upr = metx[3], sd = NA, model = NA, control_for_starting_distance = "no", Country = "Combined\n(metanalytical)", N = NA)
ogo = rbind(ogo, ogo_met, ogo_metx)
ogo[, Country := factor(Country, levels = rev(c("Finland", "Poland", "Czechia", "Hungary", "Australia", "Combined\n(metanalytical)", "All\n(mixed model)")))]
width_ <- .5 # spacing between error bars
#col_ <- c(brewer.pal(n = 12, name = "Paired"), "grey30", "grey80")
#Tol_bright <- c("#EE6677", "#228833", "#4477AA", "#CCBB44", "#66CCEE", "#AA3377", "#BBBBBB")
#Tol_muted <- c("#88CCEE", "#44AA99", "#117733", "#332288", "#DDCC77", "#999933", "#CC6677", "#882255", "#AA4499", "#DDDDDD")
#Tol_light <- c("#BBCC33", "#AAAA00", "#77AADD", "#EE8866", "#EEDD88", "#FFAABB", "#99DDFF", "#44BB99", "#DDDDDD")
# From Color Universal Design (CUD): https://jfly.uni-koeln.de/color/
#Okabe_Ito <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#000000")
#col_ = Okabe_Ito[7:1]
# JAMA and LocusZoom modified order
#col_ = c("#374E55FF", "#374E55FF", "#DF8F44FF", "#79AF97FF", "#00A1D5FF", "#B24745FF", "#80796BFF") #"#6A6599FF",
#col_ <- c("#357EBDFF", "#9632B8FF", "#46B8DAFF", "#5CB85CFF", "#EEA236FF", "#D43F3AFF", "#D43F3AFF")[7:1] # "#D43F3AFF", "#B8B8B8FF"
col_ = c("#357EBDFF", "#D43F3AFF", "#46B8DAFF", "#5CB85CFF", "#EEA236FF", "#9632B8FF", "#9632B8FF")[7:1] # "#D43F3AFF", "#B8B8B8FF"
#show_col(col_)
#p =
ggplot(ogo, aes(x = estimate, y = Country, col = Country, shape = control_for_starting_distance)) +
geom_vline(xintercept = 0, color = "grey", linetype = "dotted") +
geom_errorbarh(aes(xmin = lwr, xmax = upr), height = 0, position = ggstance::position_dodgev(width_)) +
# geom_point(position = ggstance::position_dodgev(.6)) +
geom_point(position = position_dodge(width = width_), bg = "white", size = 1.1) +
# scale_color_viridis(discrete=TRUE, begin=0, end = 0.5) +
# scale_fill_viridis(discrete=TRUE, begin=0, end = 0.5) +
# geom_text( aes(x = n_pos,label = N), vjust = 0, size = 1.75, position = ggstance::position_dodgev(width_))+ # 3 positions for 3 bars
# annotate("text", x=log10(3), y=85, label= "Used", col = "grey30", size = 2.5)+
scale_shape_manual(name = "Controlled for\nstarting distance", guide = guide_legend(reverse = TRUE), values = c(21, 19)) +
#scale_color_jama(guide = "none")+ #, palette = 'light'
scale_color_manual(guide = "none", values = col_) + #guide_legend(reverse = TRUE)
scale_x_continuous(breaks = round(seq(-0.3, 0.2, by = 0.1), 1)) +
ylab("") +
xlab("Standardized effect size of Google Mobility (human presence)\n[on flight initiation distance]") +
# coord_cartesian(xlim = c(-.15, .15)) +
# scale_x_continuous(breaks = round(seq(-.15, .15, by = 0.05),2)) +
theme_bw() +
theme(
legend.position = "right",
legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
# legend.spacing.y = unit(0.1, 'cm'),
legend.key.height = unit(0.5, "line"),
legend.margin = margin(0, 0, 0, 0),
# legend.position=c(0.5,1.6),
plot.title = element_text(color = "grey", size = 7),
plot.margin = margin(b = 0.5, l = 0.5, t = 0.5, r = 0.5, unit = "pt"),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = ax_lines, size = 0.25),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.x = element_line(colour = ax_lines, size = 0.25),
# axis.text.x = element_text()
axis.ticks.length = unit(1, "pt"),
axis.text.x = element_text(, size = 6),
axis.text.y = element_text(colour = "black", size = 7),
axis.title = element_text(size = 7)
)
ggsave(here::here("Outputs/Fig_Google_rev_width_CustomLocusZoom_v2.png"), width = 8, height = 6, unit = "cm", dpi = 600)Fig. S_Google | The effect size with 95%CIs of Google Mobility on flight initiation distance (ln-transformed). The general model on all data was controlled for year, starting distance (ln-transformed; filled circles) or not (empty circles), flock size (ln-transformed), temperature (also a proxy for a day within the breeding season: r Pearson = 0.48; Fig. S1) and time of day. To account for circular properties of time, time was transformed into radians (2 × time × π/24) and fitted as sine and cosine of radians (Bulla et al. 2016). All continuous variables were standardised by subtracting the mean and dividing by the standard deviation. The multicollinearity was small as correlations between predictors were weak (r<XX, Fig. S1). To account for the non-independence of data points (Schielzeth & Forstmeier 2009; Barr et al. 2013), we fitted random intercepts of genus, species, species at a given day and year, site, and species within a site and in the full model also random slope of Google Mobility within genus and country. Note that in the full model, fitting Google Mobility as random slope at other random intercepts produces similar results (see Fig Sxx). We used this approach with a full dataset with all observations (n = xx), as well as with conservative datasets, one with at least five observations per species , the other with at least 10 observations per species. Albeit random structure of the full model accounts for the potential country specific effect (country accounted for xx% of variance, Table xx), depicted are also estimates from country specific models, with same predictors and random structure as the full model (excluding the random slopes and also random intercepts of country and in case of Poland site, as specific sites were not noted in Poland). Using meta.summaries function from rmeta R-package we used the country estimates, their standard deviation, and sample size per country to estimate the meta-analytical mean, which reflects the results based on the full mixed effect model. Importantly, the models test for within year changes in esccape distancces due to changes in human presence, i.e. day to day changes in Google Mobility. In other words, the models investigate whether birds flexibly adjust their fear response on a day to day basis (regardless of COVID-19 restrictions), a test different to the one intended in this study. The effect sizes are small, mostly close to zero, and hihgly uncertain.
Table FID_g | Escape distance in relations to Google Mobilitty
out_FID_g$R2_mar=out_FID_g$R2_con = NULL
out_FID_g %>%
kbl() %>%
kable_paper("hover", full_width = F)| model | response | error_structure | N | type | effect | estimate_r | lwr_r | upr_r |
|---|---|---|---|---|---|---|---|---|
| Table S_FID_g - full a | Escape distance | Gaussian | 3644 | fixed | (Intercept) | 0.021 | -0.095 | 0.142 |
| fixed | year | 0.017 | -0.024 | 0.059 | ||||
| fixed | starting distance (ln)) | 0.498 | 0.466 | 0.53 | ||||
| fixed | flock size (ln) | -0.002 | -0.028 | 0.024 | ||||
| fixed | body mass (ln) | 0.027 | -0.039 | 0.092 | ||||
| fixed | sine of radians | -0.009 | -0.049 | 0.032 | ||||
| fixed | cosine of radians | 0.011 | -0.022 | 0.043 | ||||
| fixed | temperaturre | 0.022 | -0.015 | 0.06 | ||||
| fixed | Google Mobility | -0.096 | -0.228 | 0.034 | ||||
| random % | species within day & year (Intercept) | 6% | 5% | 7% | ||||
| random % | species within site (Intercept) | 5% | 4% | 5% | ||||
| random % | species (Intercept) | 7% | 7% | 7% | ||||
| random % | site (Intercept) | 9% | 9% | 9% | ||||
| random % | genus (Intercept) | 0% | -1% | 4% | ||||
| random % | genus Google Mobility | 0% | -1% | 4% | ||||
| random % | Country (Intercept) | 1% | -4% | 4% | ||||
| random % | Country Google Mobility | 1% | -4% | 4% | ||||
| random % | Residual | 71% | 59% | 82% | ||||
| Table S_FID_g - full b | Escape distance | Gaussian | 3644 | fixed | (Intercept) | 0.108 | -0.143 | 0.361 |
| fixed | year | 0.079 | 0.027 | 0.131 | ||||
| fixed | flock size (ln) | 0.012 | -0.017 | 0.042 | ||||
| fixed | body mass (ln) | 0.132 | 0.049 | 0.217 | ||||
| fixed | sine of radians | 0.066 | 0.02 | 0.113 | ||||
| fixed | cosine of radians | 0.075 | 0.037 | 0.113 | ||||
| fixed | temperaturre | 0.053 | 0.005 | 0.1 | ||||
| fixed | scale(StringencyIndex) | 0.045 | -0.028 | 0.118 | ||||
| fixed | Google Mobility | -0.117 | -0.302 | 0.074 | ||||
| random % | species within day & year (Intercept) | 7% | 5% | 9% | ||||
| random % | species within site (Intercept) | 6% | 4% | 8% | ||||
| random % | species (Intercept) | 10% | 8% | 12% | ||||
| random % | site (Intercept) | 13% | 10% | 15% | ||||
| random % | genus (Intercept) | 0% | -1% | 5% | ||||
| random % | genus Google Mobility | 0% | -1% | 5% | ||||
| random % | Country (Intercept) | 1% | -16% | 10% | ||||
| random % | Country Google Mobility | 1% | -16% | 10% | ||||
| random % | Residual | 62% | 41% | 89% | ||||
| Table S_FID_g - FI a | Escape distance | Gaussian | 322 | fixed | (Intercept) | 0.023 | -0.194 | 0.232 |
| fixed | year | 0.13 | -0.011 | 0.272 | ||||
| fixed | starting distance (ln)) | 0.194 | 0.087 | 0.301 | ||||
| fixed | flock size (ln) | -0.119 | -0.219 | -0.015 | ||||
| fixed | body mass (ln) | 0.238 | 0.086 | 0.399 | ||||
| fixed | sine of radians | 0.132 | -0.012 | 0.272 | ||||
| fixed | cosine of radians | 0.042 | -0.081 | 0.163 | ||||
| fixed | temperaturre | -0.004 | -0.139 | 0.131 | ||||
| fixed | Google Mobility | -0.1 | -0.247 | 0.036 | ||||
| random % | species within site (Intercept) | 13% | 12% | 13% | ||||
| random % | species within day & year (Intercept) | 3% | 2% | 3% | ||||
| random % | site (Intercept) | 17% | 13% | 19% | ||||
| random % | species (Intercept) | 0% | 0% | 0% | ||||
| random % | genus (Intercept) | 2% | 1% | 4% | ||||
| random % | Residual | 66% | 62% | 71% | ||||
| Table S_FID_g - FI b | Escape distance | Gaussian | 322 | fixed | (Intercept) | -0.004 | -0.226 | 0.225 |
| fixed | year | 0.153 | 0.009 | 0.295 | ||||
| fixed | flock size (ln) | -0.113 | -0.216 | -0.009 | ||||
| fixed | body mass (ln) | 0.317 | 0.158 | 0.479 | ||||
| fixed | sine of radians | 0.119 | -0.024 | 0.263 | ||||
| fixed | cosine of radians | 0.03 | -0.091 | 0.156 | ||||
| fixed | temperaturre | -0.017 | -0.153 | 0.116 | ||||
| fixed | Google Mobility | -0.088 | -0.236 | 0.054 | ||||
| random % | species within site (Intercept) | 14% | 14% | 15% | ||||
| random % | species within day & year (Intercept) | 3% | 3% | 3% | ||||
| random % | site (Intercept) | 18% | 15% | 21% | ||||
| random % | species (Intercept) | 0% | 0% | 1% | ||||
| random % | genus (Intercept) | 2% | 1% | 4% | ||||
| random % | Residual | 62% | 57% | 67% | ||||
| Table S_FID_g - PL a | Escape distance | Gaussian | 329 | fixed | (Intercept) | 0.002 | -0.127 | 0.135 |
| fixed | year | -0.189 | -0.327 | -0.048 | ||||
| fixed | starting distance (ln)) | 0.532 | 0.446 | 0.62 | ||||
| fixed | flock size (ln) | -0.023 | -0.107 | 0.061 | ||||
| fixed | body mass (ln) | -0.097 | -0.203 | 0.013 | ||||
| fixed | sine of radians | 0.109 | -0.14 | 0.357 | ||||
| fixed | cosine of radians | -0.163 | -0.414 | 0.091 | ||||
| fixed | temperaturre | -0.175 | -0.273 | -0.077 | ||||
| fixed | Google Mobility | 0.004 | -0.117 | 0.125 | ||||
| random % | species within day & year (Intercept) | 13% | 13% | 14% | ||||
| random % | species (Intercept) | 7% | 5% | 10% | ||||
| random % | genus (Intercept) | 0% | 0% | 0% | ||||
| random % | Residual | 79% | 76% | 82% | ||||
| Table S_FID_g - PL b | Escape distancey | Gaussian | 329 | fixed | (Intercept) | 0.001 | -0.202 | 0.204 |
| fixed | year | -0.378 | -0.534 | -0.222 | ||||
| fixed | flock size (ln) | 0.059 | -0.037 | 0.158 | ||||
| fixed | body mass (ln) | -0.023 | -0.178 | 0.126 | ||||
| fixed | sine of radians | -0.009 | -0.31 | 0.284 | ||||
| fixed | cosine of radians | -0.036 | -0.334 | 0.262 | ||||
| fixed | temperaturre | -0.159 | -0.272 | -0.045 | ||||
| fixed | Google Mobility | 0.054 | -0.087 | 0.197 | ||||
| random % | species within day & year (Intercept) | 7% | 7% | 7% | ||||
| random % | species (Intercept) | 21% | 16% | 26% | ||||
| random % | genus (Intercept) | 0% | 0% | 0% | ||||
| random % | Residual | 72% | 67% | 77% | ||||
| Table S_FID_g - CZ a | Escape distance | Gaussian | 1054 | fixed | (Intercept) | 0.227 | 0.02 | 0.438 |
| fixed | starting distance (ln)) | 0.345 | 0.287 | 0.402 | ||||
| fixed | flock size (ln) | 0.01 | -0.042 | 0.059 | ||||
| fixed | body mass (ln) | 0.187 | 0.029 | 0.341 | ||||
| fixed | sine of radians | 0.062 | -0.006 | 0.132 | ||||
| fixed | cosine of radians | 0.034 | -0.022 | 0.089 | ||||
| fixed | temperaturre | 0.065 | -0.006 | 0.135 | ||||
| fixed | Google Mobility | -0.089 | -0.164 | -0.016 | ||||
| random % | species within site (Intercept) | 5% | 5% | 5% | ||||
| random % | species within day & year (Intercept) | 1% | 0% | 1% | ||||
| random % | species (Intercept) | 14% | 11% | 17% | ||||
| random % | genus (Intercept) | 14% | 10% | 18% | ||||
| random % | site (Intercept) | 5% | 4% | 6% | ||||
| random % | Residual | 61% | 54% | 69% | ||||
| Table S_FID_g - CZ b | Escape distance | Gaussian | 1054 | fixed | (Intercept) | 0.229 | 0.025 | 0.436 |
| fixed | starting distance (ln)) | 0.344 | 0.284 | 0.404 | ||||
| fixed | flock size (ln) | 0.009 | -0.04 | 0.059 | ||||
| fixed | body mass (ln) | 0.182 | 0.031 | 0.343 | ||||
| fixed | sine of radians | 0.062 | -0.009 | 0.131 | ||||
| fixed | cosine of radians | 0.035 | -0.021 | 0.09 | ||||
| fixed | temperaturre | 0.065 | -0.002 | 0.136 | ||||
| fixed | Google Mobility | -0.091 | -0.163 | -0.018 | ||||
| random % | species within site (Intercept) | 5% | 5% | 5% | ||||
| random % | species within day & year (Intercept) | 1% | 0% | 1% | ||||
| random % | species (Intercept) | 14% | 12% | 17% | ||||
| random % | genus (Intercept) | 14% | 11% | 18% | ||||
| random % | site (Intercept) | 5% | 4% | 6% | ||||
| random % | Residual | 61% | 54% | 69% | ||||
| Table S_FID_g - HU a | Escape distance | Gaussian | 874 | fixed | (Intercept) | 0.085 | -0.183 | 0.35 |
| fixed | year | 0.19 | 0.078 | 0.302 | ||||
| fixed | starting distance (ln)) | 0.506 | 0.443 | 0.567 | ||||
| fixed | flock size (ln) | 0.001 | -0.053 | 0.053 | ||||
| fixed | body mass (ln) | 0 | -0.142 | 0.139 | ||||
| fixed | sine of radians | -0.122 | -0.191 | -0.057 | ||||
| fixed | cosine of radians | 0.024 | -0.041 | 0.088 | ||||
| fixed | temperaturre | -0.029 | -0.109 | 0.05 | ||||
| fixed | Google Mobility | -0.028 | -0.128 | 0.07 | ||||
| random % | species within day & year (Intercept) | 3% | 3% | 3% | ||||
| random % | species within site (Intercept) | 0% | 0% | 0% | ||||
| random % | species (Intercept) | 1% | 1% | 1% | ||||
| random % | genus (Intercept) | 11% | 7% | 15% | ||||
| random % | site (Intercept) | 9% | 6% | 13% | ||||
| random % | Residual | 75% | 67% | 82% | ||||
| Table S_FID_g - HU b | Escape distance | Gaussian | 874 | fixed | (Intercept) | 0.092 | -0.353 | 0.52 |
| fixed | year | 0.321 | 0.187 | 0.457 | ||||
| fixed | flock size (ln) | 0 | -0.058 | 0.059 | ||||
| fixed | body mass (ln) | 0.284 | 0.066 | 0.511 | ||||
| fixed | sine of radians | -0.157 | -0.233 | -0.08 | ||||
| fixed | cosine of radians | 0.033 | -0.039 | 0.104 | ||||
| fixed | temperaturre | -0.046 | -0.138 | 0.052 | ||||
| fixed | Google Mobility | -0.107 | -0.23 | 0.014 | ||||
| random % | species within day & year (Intercept) | 5% | 5% | 5% | ||||
| random % | species within site (Intercept) | 0% | 0% | 0% | ||||
| random % | species (Intercept) | 1% | 1% | 2% | ||||
| random % | genus (Intercept) | 24% | 18% | 30% | ||||
| random % | site (Intercept) | 16% | 13% | 18% | ||||
| random % | Residual | 54% | 45% | 63% | ||||
| Table S_FID_g - AU a | Escape distance | Gaussian | 1065 | fixed | (Intercept) | -0.056 | -0.197 | 0.086 |
| fixed | year | 0.028 | -0.034 | 0.092 | ||||
| fixed | starting distance (ln)) | 0.499 | 0.448 | 0.552 | ||||
| fixed | flock size (ln) | 0.025 | -0.024 | 0.074 | ||||
| fixed | body mass (ln) | -0.05 | -0.145 | 0.044 | ||||
| fixed | sine of radians | -0.044 | -0.117 | 0.028 | ||||
| fixed | cosine of radians | -0.043 | -0.112 | 0.028 | ||||
| fixed | temperaturre | 0.019 | -0.038 | 0.076 | ||||
| fixed | Google Mobility | -0.035 | -0.092 | 0.021 | ||||
| random % | species within day & year (Intercept) | 6% | 6% | 6% | ||||
| random % | species within site (Intercept) | 12% | 12% | 12% | ||||
| random % | species (Intercept) | 12% | 10% | 13% | ||||
| random % | genus (Intercept) | 2% | 1% | 2% | ||||
| random % | site (Intercept) | 8% | 6% | 10% | ||||
| random % | Residual | 61% | 57% | 65% | ||||
| Table S_FID_g - AU b | Escape distancey | Gaussian | 1065 | fixed | (Intercept) | -0.108 | -0.302 | 0.08 |
| fixed | year | 0.082 | 0.01 | 0.157 | ||||
| fixed | flock size (ln) | 0.055 | 0 | 0.11 | ||||
| fixed | body mass (ln) | 0.068 | -0.059 | 0.196 | ||||
| fixed | sine of radians | -0.031 | -0.116 | 0.054 | ||||
| fixed | cosine of radians | -0.017 | -0.097 | 0.063 | ||||
| fixed | temperaturre | 0.007 | -0.056 | 0.073 | ||||
| fixed | Google Mobility | -0.018 | -0.082 | 0.045 | ||||
| random % | species within day & year (Intercept) | 7% | 7% | 7% | ||||
| random % | species within site (Intercept) | 11% | 10% | 11% | ||||
| random % | species (Intercept) | 14% | 12% | 16% | ||||
| random % | genus (Intercept) | 6% | 5% | 7% | ||||
| random % | site (Intercept) | 10% | 8% | 12% | ||||
| random % | Residual | 52% | 48% | 58% |
Continuous variables were z-transformed.
Genus and spcies ‘raw’ data visualisations
dxx <- d[paste(IDLocality, Species) %in% paste(pp$IDLocality, pp$Species)]
# length(dxx[, unique(paste(sp_loc, Covid))])
m <- lm(log(FID) ~ log(SD), dxx)
dxx[, resid_FID := resid(m)]
a <- dxx[, .(mean(resid_FID), sd(resid_FID), mean(FID), .N), by = .(Country, IDLocality, genus, Species, sp_loc, Covid)]
setnames(a, old = c("V1", "V2", "V3"), new = c("resid_FID_avg", "SD", "FID_avg"))
a[is.na(SD), SD := 0]
aw <- reshape(a, idvar = c("Country", "IDLocality", "genus", "Species", "sp_loc"), timevar = "Covid", direction = "wide")
aw[, Species := gsub("[_]", " ", Species)]
aw <- merge(aw, t, all.x = TRUE)
#table(aw$Family)
x <- aw[, .N, by = Species]
#x[order(Species)]
aw[, genus2 := genus]
aw[Species %in% x[N %in% c(1, 2), Species], genus2 := "other"]
#aw[genus2 == "Phoenicurus", unique(Species)]
ph[genus2 == "Motacilla" | uid %in% c("67a9ecfd-58ba-44a4-9986-243b6e610419"), uid := "cf522e02-35cc-44f5-841c-0e642987c2e4"]
ph[genus2 == "Sylvia", uid := "67a9ecfd-58ba-44a4-9986-243b6e610419"]
ph[, size := 0.2]
ph[genus2 %in% c("Anas", "Columba", "Dendrocopos", "Sturnus"), size := c(0.25, 0.25, 0.15, 0.1)]
ph[, FID_avg.0 := 1.5]
ph[, FID_avg.1 := 20]
ph[genus2 %in% c("Anas", "Columba"), FID_avg.0 := c(1.7, 1.7)]
ph[, resid_FID_avg.0 := -1.7]
ph[, resid_FID_avg.1 := 0.7]
ph[genus2 %in% c("Anas", "Columba"), resid_FID_avg.0 := c(-1.6, -1.6)]
ph[, genus2 := factor(genus2, levels = c("Anas", "Larus", "Columba", "Dendrocopos", "Picus", "Motacilla", "Erithacus", "Phoenicurus", "Turdus", "Sylvia", "Parus", "Sitta", "Pica", "Garrulus", "Corvus", "Sturnus", "Passer", "Fringilla", "other"))]
aw[, genus2 := factor(genus2, levels = c("Anas", "Larus", "Columba", "Dendrocopos", "Picus", "Motacilla", "Erithacus", "Phoenicurus", "Turdus", "Sylvia", "Parus", "Sitta", "Pica", "Garrulus", "Corvus", "Sturnus", "Passer", "Fringilla", "other"))]
aw[, genus2 := factor(genus2, levels = c("Anas", "Larus", "Columba", "Dendrocopos", "Picus", "Motacilla", "Erithacus", "Phoenicurus", "Turdus", "Sylvia", "Parus", "Sitta", "Pica", "Garrulus", "Corvus", "Sturnus", "Passer", "Fringilla", "other"))]
# Fig?? and left panel of S5- plot from files
anas <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Anas.png"))))
columba <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Columba.png"))))
Dendrocopos <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Dendrocopos.png"))))
Larus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Larus_flip.png"))))
Picus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Picus.png"))))
Motacilla <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Motacilla.png"))))
Erithacus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Erithacus.png"))))
Phoenicurus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Phoenicurus.png"))))
Turdus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Turdus.png"))))
Sylvia <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Sylvia.png"))))
Parus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Parus_flip.png"))))
Sitta <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Sitta.png"))))
Pica <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Pica.png"))))
Garrulus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Garrulus.png"))))
Corvus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Corvus.png"))))
Sturnus <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Sturnus.png"))))
Passer <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Passer_flip.png"))))
Fringilla <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/Fringilla.png"))))
other <- rasterGrob(change_col("#CCCCCC", readPNG(here::here("Data/Pics/other_flip.png"))))
ann_text <- data.frame(
FID_avg.0 = 8, FID_avg.1 = 10, lab = "Text",
genus2 = factor("Anas", levels = c("Anas", "Larus", "Columba", "Dendrocopos", "Picus", "Motacilla", "Erithacus", "Phoenicurus", "Turdus", "Sylvia", "Parus", "Sitta", "Pica", "Garrulus", "Corvus", "Sturnus", "Passer", "Fringilla", "other"))
)
ann_text2 <- data.frame(
FID_avg.0 = 6, FID_avg.1 = 3, lab = "Text",
genus2 = factor("Larus", levels = c("Anas", "Larus", "Columba", "Dendrocopos", "Picus", "Motacilla", "Erithacus", "Phoenicurus", "Turdus", "Sylvia", "Parus", "Sitta", "Pica", "Garrulus", "Corvus", "Sturnus", "Passer", "Fringilla", "other"))
)
aw2 <- data.frame(FID_avg.0 = c(11.25, 11.25), FID_avg.1 = c(3.5, 5.8), genus2 = factor("Larus", levels = c("Anas", "Larus", "Columba", "Dendrocopos", "Picus", "Motacilla", "Erithacus", "Phoenicurus", "Turdus", "Sylvia", "Parus", "Sitta", "Pica", "Garrulus", "Corvus", "Sturnus", "Passer", "Fringilla", "other")))
col3_ <- c("#357EBDFF", "#D43F3AFF", "#46B8DAFF", "#5CB85CFF", "#EEA236FF", "#9632B8FF", "#9632B8FF")[7:1]
col3__ <- col3_[3:7]
aw[, Country := factor(Country, levels = rev(c("Finland", "Poland", "Czechia", "Hungary", "Australia")))]
g_gen <-
ggplot(aw, aes(x = FID_avg.0, y = FID_avg.1)) +
# geom_errorbar(aes(ymin = FID_avg.1-SD.1, ymax = FID_avg.1+SD.1, col = Country), width = 0) +
# geom_errorbar(aes(xmin = FID_avg.0-SD.0, xmax = FID_avg.0+SD.0, col = Country), width = 0) +
# geom_point(pch = 21, alpha = 0.7, aes(col = Country)) +
annotation_custom2(anas, data = ph[genus2 == "Anas"], xmin = 0.05, xmax = 0.5, ymax = 2.6) +
annotation_custom2(Larus, data = ph[genus2 == "Larus"], xmin = 0.05, xmax = 0.5, ymax = 2.6) +
annotation_custom2(columba, data = ph[genus2 == "Columba"], xmin = 0.05, xmax = 0.4, ymax = 2.7) +
annotation_custom2(Dendrocopos, data = ph[genus2 == "Dendrocopos"], xmin = 0.05, xmax = 0.25, ymax = 2.6) +
annotation_custom2(Picus, data = ph[genus2 == "Picus"], xmin = 0.05, xmax = 0.4, ymax = 2.7) +
annotation_custom2(Motacilla, data = ph[genus2 == "Motacilla"], xmin = 0.05, xmax = 0.5, ymax = 2.7) +
annotation_custom2(Erithacus, data = ph[genus2 == "Erithacus"], xmin = 0.05, xmax = 0.35, ymax = 2.7) +
annotation_custom2(Phoenicurus, data = ph[genus2 == "Phoenicurus"], xmin = 0.05, xmax = 0.35, ymax = 2.7) +
annotation_custom2(Turdus, data = ph[genus2 == "Turdus"], xmin = 0.05, xmax = 0.5, ymax = 2.7) +
annotation_custom2(Sylvia, data = ph[genus2 == "Sylvia"], xmin = 0.05, xmax = 0.5, ymax = 2.7) +
annotation_custom2(Parus, data = ph[genus2 == "Parus"], xmin = 0.05, xmax = 0.42, ymax = 2.7) +
annotation_custom2(Sitta, data = ph[genus2 == "Sitta"], xmin = 0.05, xmax = 0.5, ymax = 2.7) +
annotation_custom2(Pica, data = ph[genus2 == "Pica"], xmin = 0.05, xmax = 0.5, ymax = 2.5) +
annotation_custom2(Garrulus, data = ph[genus2 == "Garrulus"], xmin = 0.05, xmax = 0.6, ymax = 2.7) +
annotation_custom2(Corvus, data = ph[genus2 == "Corvus"], xmin = 0.05, xmax = 0.4, ymax = 2.55) +
annotation_custom2(Sturnus, data = ph[genus2 == "Sturnus"], xmin = 0.05, xmax = 0.24, ymax = 2.65) +
annotation_custom2(Passer, data = ph[genus2 == "Passer"], xmin = 0.05, xmax = 0.36, ymax = 2.65) +
annotation_custom2(Fringilla, data = ph[genus2 == "Fringilla"], xmin = 0.05, xmax = 0.5, ymax = 2.7) +
annotation_custom2(other, data = ph[genus2 == "other"], xmin = 0.05, xmax = 0.45, ymax = 2.4) +
geom_point(pch = 21, alpha = 0.7, aes(fill = Country), col = 'grey20', alpha =0.8)+#col = "white") +
# ggtitle ("Sim based")+
geom_abline(intercept = 0, slope = 1, lty = 3, col = "grey80") +
geom_line(data = aw2, col = "grey80", lwd = 0.25) +
geom_text(data = ann_text, label = "No difference", col = "grey80", angle = 45, size = 2) +
geom_text(data = ann_text2, label = "Species mean / site", col = "grey60", size = 2, ) +
facet_wrap(~genus2) +
# geom_phylopic(data = o, aes(image = uid), color = "grey80", size = o$size) + # ,
#scale_fill_viridis(discrete = TRUE, guide = guide_legend(reverse = FALSE)) +
scale_fill_manual(values = col3__,guide = guide_legend(reverse = TRUE)) +
scale_x_continuous("Before COVID-19 shutdown - flight initiation distance [m]", expand = c(0, 0), trans = "log10") +
scale_y_continuous("During COVID-19 shutdown - flight initiation distance [m]", expand = c(0, 0), trans = "log10") +
# labs(title = "Species means per sampling location")+
theme_MB +
theme(
plot.title = element_text(size = 7),
strip.background = element_blank(),
# panel.spacing = unit(1, "mm"),
legend.position = c(1, 0.025),
legend.justification = c(1, -0.05)
)
gg_gen <- ggplotGrob(g_gen) # gg$layout$name
ggx_gen <- gtable_filter_remove(gg_gen,
name = paste0("axis-b-", c(2, 4), "-4"),
trim = FALSE
)
# grid.draw(ggx)
# ggsave('Outputs/Fig_2_width-114mm.png',ggx, width=4.5,height=4.5,dpi=600) # 11.43cm # with label on top
if(save_plot==TRUE){
ggsave(here::here("Outputs/Fig_2_width-122mm_col_grey_rev.png"), ggx_gen, width = 4.8, height = 4.5, dpi = 600) # 12.2cm # with label inside
} # Fig S5 right panel
g_2 <-
ggplot(aw, aes(x = resid_FID_avg.0, y = resid_FID_avg.1)) +
geom_point(pch = 21, alpha = 0.7, aes(fill = Country), col = 'grey20', alpha =0.8)+#col = "white") +
geom_abline(intercept = 0, slope = 1, lty = 3, col = "grey80") +
facet_wrap(~genus2) +
# geom_phylopic(data = o, aes(image = uid), color = "grey80", size = o$size) +
#scale_fill_viridis(discrete = TRUE, guide = guide_legend(reverse = TRUE)) +
scale_fill_manual(values = col3__,guide = guide_legend(reverse = TRUE)) +
scale_x_continuous("Before COVID-19 shutdown - residual escape distance", expand = c(0, 0)) +
scale_y_continuous("During COVID-19 shutdown - residual escape distance", expand = c(0, 0)) +
# labs(title = "Species means per sampling location")+
theme_MB +
theme(
plot.title = element_text(size = 7),
strip.background = element_blank(),
legend.position = "none",
# legend.position = c(1, 0),
legend.justification = c(1, 0)
)
g_g2 <- ggplotGrob(g_2) # gg$layout$name
g_gx2 <- gtable_filter_remove(g_g2,
name = paste0("axis-b-", c(2, 4), "-4"),
trim = FALSE
)
# Fig S5 combine
grid.draw(cbind(ggx_gen, g_gx2, size = "last"))
if(save_plot==TRUE){
ggsave(here::here("Outputs/Fig_S5_rev_v2.png"), cbind(ggx_gen, g_gx2, size = "last"), width = 4.8 * 2, height = 4.5, dpi = 600)
}exploration for Fig S5 legend
correlation between mean fid and residual fid
#ggplot(a, aes(x = resid_FID_avg)) + geom_histogram()
g_a =
ggplot(a, aes(x = FID_avg, y = resid_FID_avg)) +
labs(subtitle='Species means per period')+
stat_smooth() +
geom_point() +
stat_cor(aes(label = ..r.label..), label.x = 0.3, size = 2) +
scale_x_continuous(trans = "log")
g_r =
ggplot(dxx, aes(x = FID, y = resid_FID)) +
labs(subtitle='Raw observations')+
stat_smooth() +
geom_point() +
stat_cor(aes(label = ..r.label..), label.x = 0.3, size = 2) +
scale_x_continuous(trans = "log")
grid.draw(cbind(ggplotGrob(g_a), ggplotGrob(g_r)))
#cor(log(dxx$FID), dxx$resid_FID)
#cor(log(a$FID_avg), a$resid_FID_avg)difference in number of observations per species and site before aand during shutdowns
ggplot(aw, aes(x = N.0 - N.1)) +
geom_histogram()
#nrow(aw[abs(N.0 - N.1) > 2])
#nrow(aw[!abs(N.0 - N.1) > 2])Fig S3 - species means
aw[, sp2 := gsub(" ", "\n", Species)]
ann_text <- data.frame(FID_avg.0 = 8, FID_avg.1 = 10,lab = "Text",
Species = factor('Aegithalos caudatus',levels = levels(as.factor(aw$Species))))
ann_text$sp2 = gsub(" ", "\n", ann_text$Species)
g3 =
ggplot(aw, aes(x = FID_avg.0, y = FID_avg.1)) +
#geom_errorbar(aes(ymin = FID_avg.1-SD.1, ymax = FID_avg.1+SD.1, col = Country), width = 0) +
#geom_errorbar(aes(xmin = FID_avg.0-SD.0, xmax = FID_avg.0+SD.0, col = Country), width = 0) +
#geom_point(pch = 21, alpha = 0.7, aes(col = Country)) +
geom_point(pch = 21, alpha = 0.7, aes(fill = Country), col = 'grey20', alpha =0.8)+#col = "white") +
#ggtitle ("Sim based")+
geom_abline(intercept = 0, slope = 1, lty =3, col = "grey80")+
geom_text(data = ann_text,label = "No difference", col = "grey80",angle = 45, size = 2) +
facet_wrap(~sp2) +
#geom_phylopic(data = o, aes(image = uid), color = "grey80", size = o$size) + # ,
#scale_fill_viridis(discrete=TRUE,guide = guide_legend(reverse = FALSE)) +
scale_fill_manual(values = col3__,guide = guide_legend(reverse = TRUE)) +
scale_x_continuous("Before COVID-19 shutdown - flight initiation distance [m]", expand = c(0, 0), trans = 'log10') +
scale_y_continuous("During COVID-19 shutdown - flight initiation distance [m]", expand = c(0, 0), trans = 'log10') +
labs(title = "Species means per sampling location")+
theme_MB +
theme(
plot.title = element_text(size=7),
strip.background = element_blank(),
#panel.spacing = unit(1, "mm"),
legend.position = c(0.96, 0.0),
legend.justification = c(1, 0)
)
gg3 <- ggplotGrob(g3) #gg2$layout$name
ggx3 <- gtable_filter_remove(gg3, name = c(paste0("axis-b-", c(2, 4), "-7"), "axis-b-6-6"),
trim = FALSE)
grid.draw(ggx3)
if(save_plot==TRUE){
ggsave(here::here('Outputs/Fig_S3_species_rev_v2.png'),ggx3, width=13.5,height=17.5,unit = 'cm', dpi=600) # 11.43cm
}Figure S7 - stringency trends
ss <- s[Nsp > 9]
ss[, sp2 := gsub(" ", "\n", sp)]
ss[, Country := factor(Country, levels = rev(c("Finland", "Poland", "Czechia", "Hungary", "Australia")))]
gs2 <-
ggplot(ss, aes(x = StringencyIndex, y = FID)) +
# stat_smooth(method = 'rlm', se = FALSE, col = 'black', lwd = 0.5)+
geom_point(pch = 21, alpha = 0.7, aes(fill = Country), col = 'grey20', alpha =0.8)+
stat_smooth(se = FALSE, aes(colour = "Locally weighted\nsmoothing"), lwd = 0.5) + # show_guide=TRUE
facet_wrap(~sp2, ncol = 6) +
scale_fill_manual(values = col3__,guide = guide_legend(reverse = TRUE)) +
#scale_fill_viridis(discrete = TRUE, guide = guide_legend(reverse = FALSE)) +
scale_x_continuous("Stringency index of governmental COVID-19 restrictions", expand = c(0, 0)) +
scale_y_continuous("Flight initiation distance [m]", expand = c(0, 0), trans = "log10") +
# annotate("text", x = 1, y = 1, label = c(rep("", 52),"Observation"), hjust = -0.08, size = 1) +
# labs(title = "Species means per sampling location")+
scale_colour_manual(values = c("grey60")) +
# scale_color_manual(name = 'try', values = c('LOESS smoothed = "grey60"'))+
theme_MB +
theme(
plot.title = element_text(size = 7),
strip.background = element_blank(),
# strip.text.x = element_text(size = 4.5, color="grey30", margin=margin(1,1,1,1,"mm")),
# panel.spacing = unit(1, "mm"),
legend.position = c(1, 0.01),
legend.justification = c(1, 0),
legend.title = element_blank(),
# legend.spacing.y = unit(-0.78, "cm")
# legend.spacing.y = unit(0.02, "cm") use if LOESS smooth text as legend
legend.spacing.y = unit(-0.9, "cm")
)
gsg2 <- ggplotGrob(gs2) # gg$layout$name
gsgx2 <- gtable_filter_remove(gsg2, name = paste0("axis-b-", c(2, 4), "-9"), trim = FALSE)
grid.draw(gsgx2)
if(save_plot==TRUE){
ggsave(here::here("Outputs/Fig_3_width-152mm_2-row_v2.png"), gsgx2, width = 15.24, height = 19, unit = "cm", dpi = 600)
}Fig S8 - google trends
ss[, NspC := .N, by = "sp_country"]
ssc <- ss[NspC > 9]
ssc[, sp2 := gsub(" ", "\n", sp)]
ssc[, Google_mobility := parks_percent_change_from_baseline]
ssc[, Country := factor(Country, levels = rev(c("Finland", "Poland", "Czechia", "Hungary", "Australia")))]
# two rows labels
gt2 <-
ggplot(ssc, aes(x = Google_mobility, y = FID, )) +
# stat_smooth(method = 'rlm', se = FALSE, col = 'black', lwd = 0.5)+
geom_point(pch = 21, alpha = 0.7, aes(fill = Country), col = 'grey20', alpha =0.8)+
stat_smooth(se = FALSE, aes(colour = "Locally weighted\nsmoothing"), lwd = 0.5) + # show_guide=TRUE
facet_wrap(~sp2, ncol = 6) +
scale_fill_manual(values = col3__,guide = guide_legend(reverse = TRUE)) +
scale_colour_manual(values = c("grey60")) +
scale_x_continuous("Google Mobility", expand = c(0, 0)) +
scale_y_continuous("Flight initiation distance [m]", expand = c(0, 0), trans = "log10") +
# annotate("text", x = 1, y = 1, label = c(rep("", 52),"Observation"), hjust = -0.08, size = 1) +
# labs(title = "Species means per sampling location")+
# scale_colour_manual(values=c('grey60'))+
# scale_color_manual(name = 'try', values = c('LOESS smoothed = "grey60"'))+
theme_MB +
theme(
plot.title = element_text(size = 7),
strip.background = element_blank(),
# strip.text.x = element_text(size = 4.5, color="grey30", margin=margin(1,1,1,1,"mm")),
# panel.spacing = unit(1, "mm"),
legend.position = c(1, 0.01),
legend.justification = c(1, 0),
legend.title = element_blank(),
# legend.spacing.y = unit(-0.78, "cm")
# legend.spacing.y = unit(0.02, "cm") use if LOESS smooth text as legend
legend.spacing.y = unit(-0.9, "cm")
)
gtg2 <- ggplotGrob(gt2) # gg$layout$name
gtgx2 <- gtable_filter_remove(gtg2, name = c("axis-b-2-9", "axis-b-5-8"), trim = FALSE) # paste0("axis-b-", c(2, 4), "-9")
grid.draw(gtgx2)
if(save_plot==TRUE){
ggsave(here::here("Outputs/Fig_S8_width-152mm_Google.png"), gtgx2, width = 15.24, height = 19, unit = "cm", dpi = 600)
}Figure S2 - Alternative models give similar results
# prepare estimates Period
# 01a all data, main text
m1a <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(Covid) | Country) + (scale(Covid) | IDLocality) + (1 | sp_loc),
data = d, REML = FALSE,
control = lmerControl(
optimizer = "optimx", optCtrl = list(method = "nlminb")
)
)
est_m1a = est_out(m1a, "01a) (1|Year) + (1|weekday) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (1|sp_loc)")
# 01b all data, all random slopes - singularity
m1b=lmer(scale(log(FID))~
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(Covid)+
(1|Year) + (1 | weekday) + (scale(Covid)|genus)+(scale(Covid)|Species)+(1|sp_day_year) + (scale(Covid)|Country) +(scale(Covid)|IDLocality) + (scale(Covid)|sp_loc),
data = d, REML = FALSE
)
# # (Covid|IDLocality) +
est_m1b = est_out(m1b, "01b) (1|Year) + (1|weekday) + (scale(Covid)|genus) + (scale(Covid)|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (scale(Covid)|sp_loc)")
# 01c all data, all random slopes, but some without cor to avoid singularity
m1c=lmer(scale(log(FID))~
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(Covid)+
#(1|Year) +(0+Covid|genus)+(0+Covid|Species)+(1|sp_day_year) + (Covid|Country) + (0+Covid|IDLocality) +(Covid|sp_loc)
(1|Year)+ (1|weekday) + (0+scale(Covid)|genus)+(0+scale(Covid)|Species)+(1|sp_day_year) + (scale(Covid)|Country) +(scale(Covid)|IDLocality) + (scale(Covid)|sp_loc),
data = d, REML = FALSE) # (0+scale(Covid)|genus) is zero
est_m1c = est_out(m1c, "01c) (1|Year) + (1|weekday) + (0+scale(Covid)|genus) + (0+scale(Covid)|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (scale(Covid)|sp_loc)")
# 01d all data, random slopes that allow for non-singular fit
m1d=lmer(scale(log(FID))~
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(Covid)+
(1|Year) + (1|weekday) +(1|genus)+(1|Species)+(1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (scale(Covid)|sp_loc),
data = d, REML = FALSE)# (Covid|IDLocality) +
#d[,res := resid(mf)]
est_m1d = est_out(m1d, "01d) (1|Year) + (1|weekday)+ (1|genus) + (1|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (scale(Covid)|sp_loc)")
# 01e all data, random slopes that allow for non-singular fit with simple structure
m1e=lmer(scale(log(FID))~
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(Covid)+
(1|Year) + (1|weekday)+(1|genus)+(1|Species)+(1|sp_day_year) + (scale(Covid)|Country) + (1|IDLocality) + (1|sp_loc),
data = d, REML = FALSE)# (Covid|IDLocality) +
est_m1e = est_out(m1e, "01e) (1|Year) + (1|weekday) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (1|IDLocality) + (1|sp_loc)")
# 02a) before & during > 4/species - main text
dx = dd[N_during>4 & N_before >4]
#dxx = d[Species %in% dx$Species]
#dx2 = dd[N_during>9 & N_before >9]
m2a <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1| weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(Covid) | Country) + (scale(Covid) | IDLocality) + (1 | sp_loc),
data = d[Species %in% dx$Species], REML = FALSE,
control = lmerControl(
optimizer = "optimx", optCtrl = list(method = "nlminb")
)
)
est_m2a = est_out(m2a, "02a) (1|Year) + (1|weekday) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (1|sp_loc); >4/species/period")
# 02b) before & during > 4/species - singularity
dx = dd[N_during > 4 & N_before > 4]
m2b=lmer(scale(log(FID))~
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(Covid)+
(1|Year) + (1| weekday)+ (scale(Covid)|genus)+(scale(Covid)|Species)+(1|sp_day_year) + (scale(Covid)|Country) +(scale(Covid)|IDLocality) + (scale(Covid)|sp_loc),
#(1|Year) + (1|genus)+(1|Species)+(1|sp_day_year) + (Covid|Country) + (Covid|sp_loc),
data = d[Species %in% dx$Species],
REML = FALSE) # (Covid|IDLocality) +
est_m2b = est_out(m2b, "02b) (1|Year) + (1|weekday)+ (scale(Covid)|genus)+(scale(Covid)|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (scale(Covid)|sp_loc); >4/species/period")
# 02c) before & during > 4/species, random slopes that allow for non-singular fit and simple structure
dx = dd[N_during>4 & N_before >4]
#dxx = d[Species %in% dx$Species]
#dx2 = dd[N_during>9 & N_before >9]
m2c=lmer(scale(log(FID))~
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(Covid)+
(1|Year)+ (1| weekday) +(1|genus)+(1|Species)+(1|sp_day_year) + (scale(Covid)|Country) + (1|IDLocality) + (1|sp_loc),
#(1|Year) + (1|genus)+(1|Species)+(1|sp_day_year) + (Covid|Country) + (Covid|sp_loc),
data = d[Species %in% dx$Species],
REML = FALSE) # (Covid|IDLocality) +
est_m2c = est_out(m2c, "02c) (1|Year) + (1|weekday)+ (1|genus) + (1|Species)+(1|sp_day_year) + (scale(Covid)|Country) + (1|IDLocality) + (1|sp_loc); >4/species/period")
# 03a) before & during > 9/species - main text
dx = dd[N_during > 9 & N_before > 9]
m3a <- lmer(scale(log(FID)) ~
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(Covid) +
(1 | Year) + (1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(Covid) | Country) + (scale(Covid) | IDLocality) + (1 | sp_loc),
data = d[Species %in% dx$Species], REML = FALSE,
)
est_m3a = est_out(m3a, "03a) (1|Year) + (1|weekday) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (1|sp_loc); >9/species/period")
# 03b) before & during > 9/species - singularity
dx = dd[N_during>9 & N_before >9]
#dxx = d[Species %in% dx$Species]
#dx2 = dd[N_during>9 & N_before >9]
m3b=lmer(scale(log(FID))~
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(Covid)+
(1|Year) + (1| weekday) + (scale(Covid)|genus)+(scale(Covid)|Species)+(1|sp_day_year) + (scale(Covid)|Country) +(scale(Covid)|IDLocality) + (scale(Covid)|sp_loc),
#(1|Year) + (1|genus)+(1|Species)+(1|sp_day_year) + (Covid|Country) + (Covid|sp_loc),
data = d[Species %in% dx$Species],
REML = FALSE) # (Covid|IDLocality) +
est_m3b = est_out(m3b, "03b) (1|Year) + (1|weekday) + (scale(Covid)|genus)+(scale(Covid)|Species) + (1|sp_day_year) + (scale(Covid)|Country) + (scale(Covid)|IDLocality) + (scale(Covid)|sp_loc); >9/species/period")
# 03c) before & during > 9/species, random slopes that allow for non-singular fit and simple structure
dx = dd[N_during>9 & N_before >9]
#dxx = d[Species %in% dx$Species]
#dx2 = dd[N_during>9 & N_before >9]
m3c=lmer(scale(log(FID))~
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(Covid)+
(1|Year) + (1| weekday) +(1|genus)+(1|Species)+(1|sp_day_year) + (scale(Covid)|Country) + (1|IDLocality) + (1|sp_loc),
#(1|Year) + (1|genus)+(1|Species)+(1|sp_day_year) + (Covid|Country) + (Covid|sp_loc),
data = d[Species %in% dx$Species],
REML = FALSE) # (Covid|IDLocality) +
est_m3c = est_out(m3c, '03c) (1|Year) + (1|genus) + (1|Species)+(1|sp_day_year) + (scale(Covid)|Country) + (1|IDLocality) + (1|sp_loc); >9/species/period')
# prepare estimates Stringency
m01a <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1|weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(StringencyIndex) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = s, REML = FALSE)
est_m01a = est_out(m01a, "01a) (1|weekday) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc)")
m01b=lmer(scale(log(FID))~
scale(Year)+
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(StringencyIndex)+
(1|weekday) + (scale(StringencyIndex)|genus)+(1|Species)+(1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc),
data = s, REML = FALSE
)
# (1|Year) explains nothing - could stay
est_m01b = est_out(m01b, "01b) (1|weekday) + (scale(StringencyIndex)|genus) + (1|Species) + (1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc)")
m01c=lmer(scale(log(FID))~
scale(Year)+
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(StringencyIndex)+
(scale(StringencyIndex)|Country) + (1|IDLocality),
data = s, REML = FALSE)
# (1|Year), (1|genus), (1|sp_day_year), (1|sp_loc), explain nothing - could stay
est_m01c = est_out(m01c, "01c) (scale(StringencyIndex)|Country) + (1|IDLocality)")
m02a <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(StringencyIndex) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = s[Nsp>4], REML = FALSE
)
est_m02a = est_out(m02a, "02a) (1|weekday) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc); >4/species")
m02b=lmer(scale(log(FID))~
scale(Year)+
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(StringencyIndex)+
(1|weekday) + (scale(StringencyIndex)|genus)+(1|Species)+(1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc),
data = s[Nsp>4], REML = FALSE,
control = lmerControl(
optimizer ='optimx', optCtrl=list(method='nlminb'))
)
# (1|Year) explains nothing - could stay
est_m02b = est_out(m02b, "02b) (1|weekday) + (scale(StringencyIndex)|genus) + (1|Species) + (1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc); >4/species")
m02c=lmer(scale(log(FID))~
scale(Year)+
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(StringencyIndex)+
(scale(StringencyIndex)|Country) + (1|IDLocality),
data = s[Nsp>4], REML = FALSE)
# (1|Year), (1|genus), (1|sp_day_year), (1|sp_loc), explain nothing - could stay
est_m02c = est_out(m02c, "02c) (scale(StringencyIndex)|Country) + (1|IDLocality); >4/species")
m03a <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(StringencyIndex) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = s[Nsp > 9], REML = FALSE
)
est_m03a = est_out(m03a, "03a) (1|weekday) + (1|genus) + (1|Species) + (1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc); >9/species")
m03b = lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(1 | weekday) + (scale(StringencyIndex) | genus) + (1 | Species) + (1 | sp_day_year) + (scale(StringencyIndex) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = s[Nsp > 9], REML = FALSE
)
est_m03b = est_out(m03b, "03b) (1|weekday) + (scale(StringencyIndex)|genus) + (1|Species) + (1|sp_day_year) + (scale(StringencyIndex)|Country) + (1|IDLocality) +(1|sp_loc); >9/species")
m03c = lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(StringencyIndex) +
(scale(StringencyIndex) | Country) + (1 | IDLocality),
data = s[Nsp > 9], REML = FALSE
)
# (1|Year), (1|genus), (1|sp_day_year), (1|sp_loc), explain nothing - could stay
est_m03c = est_out(m03c, "03c) (scale(StringencyIndex)|Country) + 1|IDLocality); >9/species")
# prepare estimates google mobility
mg01a=lmer(scale(log(FID))~
scale(Year)+
scale(log(SD))+
scale(log(FlockSize))+
scale(log(BodyMass))+
scale(sin(rad)) + scale(cos(rad)) +
#scale(Day)+
scale(Temp)+
scale(parks_percent_change_from_baseline)+
(1|weekday) + (1|genus) +(1|Species) + (1|sp_day_year) + (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality)+(1|sp_loc),
data = ss, REML = FALSE
)
# (1|Year), (1|genus), (1|sp_day_year), (1|sp_loc), explain nothing - could stay
est_mg01a = est_out(mg01a, "01a) (1|weekday) + (1|genus) +(1|Species) + (1|sp_day_year) + (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality)+(1|sp_loc)")
mg01b <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1|weekday) + (scale(parks_percent_change_from_baseline)| genus) + (1 | Species) + (1 | sp_day_year) +
(scale(parks_percent_change_from_baseline)| Country) + (1 | IDLocality) + (1 | sp_loc),
data = ss, REML = FALSE
)
est_mg01b = est_out(mg01b, "01b) (1|weeekday) + (scale(parks_percent_change_from_baseline)|genus)+(1|Species)+(1|sp_day_year) + (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality) +(1|sp_loc)")
mg01c <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(scale(parks_percent_change_from_baseline) | Country) + (1 | IDLocality),
data = ss, REML = FALSE
)
est_mg01c = est_out(mg01c, "01c) (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality)")
mg02a = lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(parks_percent_change_from_baseline) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = ss[Nsp>4], REML = FALSE
)
# (1|Year), (1|genus), (1|sp_day_year), (1|sp_loc), explain nothing - could stay
est_mg02a = est_out(mg02a, "02a) (1|weekday) + (1|genus) +(1|Species) + (1|sp_day_year) + (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality)+(1|sp_loc); >4/specie")
mg02b <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | weekday) + (scale(parks_percent_change_from_baseline) | genus) + (1 | Species) + (1 | sp_day_year) +
(scale(parks_percent_change_from_baseline) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = ss[Nsp>4], REML = FALSE
)
est_mg02b = est_out(mg02b, "02b) (1|weeekday) + (scale(parks_percent_change_from_baseline)|genus)+(1|Species)+(1|sp_day_year) + (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality) +(1|sp_loc); >4/specie")
mg02c <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(scale(parks_percent_change_from_baseline) | Country) + (1 | IDLocality),
data = ss[Nsp>4], REML = FALSE
)
est_mg02c = est_out(mg02c, "02c) (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality); >4/specie")
mg03a = lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | weekday) + (1 | genus) + (1 | Species) + (1 | sp_day_year) + (scale(parks_percent_change_from_baseline) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = ss[Nsp > 9], REML = FALSE
)
# (1|Year), (1|genus), (1|sp_day_year), (1|sp_loc), explain nothing - could stay
est_mg03a = est_out(mg03a, "03a) (1|weekday) + (1|genus) +(1|Species) + (1|sp_day_year) + (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality)+(1|sp_loc); >9/specie")
mg03b <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(1 | weekday) + (scale(parks_percent_change_from_baseline) | genus) + (1 | Species) + (1 | sp_day_year) +
(scale(parks_percent_change_from_baseline) | Country) + (1 | IDLocality) + (1 | sp_loc),
data = ss[Nsp > 9], REML = FALSE
)
est_mg03b = est_out(mg03b, "03b) (1|weeekday) + (scale(parks_percent_change_from_baseline)|genus)+(1|Species)+(1|sp_day_year) + (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality) +(1|sp_loc); >9/specie")
mg03c <- lmer(scale(log(FID)) ~
scale(Year) +
scale(log(SD)) +
scale(log(FlockSize)) +
scale(log(BodyMass)) +
scale(sin(rad)) + scale(cos(rad)) +
# scale(Day)+
scale(Temp) +
scale(parks_percent_change_from_baseline) +
(scale(parks_percent_change_from_baseline) | Country) + (1 | IDLocality),
data = ss[Nsp > 9], REML = FALSE
)
est_mg03c = est_out(mg03c, "03c) (scale(parks_percent_change_from_baseline)|Country) + (1|IDLocality); >9/specie")
# export
save(file = here::here("Data/Fig_S2_estimates.Rdata"),
est_m1a, est_m1b, est_m1c, est_m1d, est_m1e, est_m2a, est_m2b, est_m2c, est_m3a, est_m3b, est_m3c,
est_m01a, est_m01b, est_m01c, est_m02a, est_m02b, est_m02c, est_m03a, est_m03b, est_m03c,
est_mg01a, est_mg01b, est_mg01c, est_mg02a, est_mg02b, est_mg02c, est_mg03a, est_mg03b, est_mg03c)
# prepare plot for Period
xs = rbind(est_m1a, est_m1b, est_m1c, est_m1d, est_m1e, est_m2a, est_m2b, est_m2c, est_m3a, est_m3b, est_m3c)
#gsub("scale\\(Covid\\)", "Period", "(scale(Covid)|Country)")
xs[, model := gsub("scale\\(Covid\\)", "Period", xs$model)]
xs[, model := gsub("Year", "year", xs$model)]
xs[, model := gsub("Species", "species", xs$model)]
xs[, model := gsub("sp_day_year", "species within day & year", xs$model)]
xs[, model := gsub("IDLocality", "site", xs$model)]
xs[, model := gsub("sp_loc", "species within site", xs$model)]
gs2_ =
ggplot(xs[predictor == "scale(Covid)"], aes(y = model, x = estimate, col = model)) +
geom_vline(xintercept = 0, col = "grey30", lty = 3) +
geom_errorbar(aes(xmin = lwr, xmax = upr, col = model), width = 0, position = position_dodge(width = 0.01)) +
# ggtitle ("Sim based")+
geom_point(position = position_dodge(width = 0.01)) +
# scale_colour_brewer(type = 'qual', palette = 'Paired',guide = guide_legend(reverse = TRUE))+
# scale_fill_brewer(type = 'qual', palette = 'Paired',guide = guide_legend(reverse = TRUE))+
scale_color_viridis(discrete = TRUE, guide = guide_legend(reverse = TRUE)) +
scale_fill_viridis(discrete = TRUE, guide = guide_legend(reverse = TRUE)) +
scale_y_discrete(limits = rev) +
coord_fixed(ratio = 0.05, xlim = c(-0.23, 0.15)) +
# scale_shape(guide = guide_legend(reverse = TRUE)) +
# scale_x_continuous(limits = c(-2, 2), expand = c(0, 0), breaks = seq(-2,2, by = 1), labels = seq(-2,2, by = 1)) +
labs(y = NULL, x = "Period (before vs during shutdowns)", tag = "a)") + # title = "a)",Effect of Period (before/during shutdown)
# ylim(c(0,100))+
# coord_flip()+
theme_bw() +
theme(
legend.position = "none",
#plot.title.position = "plot",
plot.subtitle = element_text(size = 7),
plot.title = element_text(size = 7),
plot.tag = element_text(size = 7),
legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
## legend.spacing.y = unit(0.1, 'cm'),
legend.key.height = unit(0.5, "line"),
# plot.margin = margin(b = 0.5, l = 0.5, t = 0.5, r =0.5, unit = "pt"),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = ax_lines, size = 0.25),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.x = element_line(colour = ax_lines, size = 0.25),
axis.ticks.length = unit(1, "pt"),
axis.text.x = element_text(colour = "black", size = 6),
axis.text.y = element_text(colour = "black", size = 7),
axis.title = element_text(size = 7)
)
#gs2
# ggsave(here::here('Outputs/Figure_Sy.png'),g, width = 30, height =5, units = 'cm')
# prepare plot for Stringency
xs0 = rbind(est_m01a, est_m01b, est_m01c, est_m02a, est_m02b, est_m02c, est_m03a, est_m03b, est_m03c)
xs0[, model := gsub("scale\\(StringencyIndex\\)", "Stringency Index", model)]
xs0[, model := gsub("Year", "year", model)]
xs0[, model := gsub("Species", "species", model)]
xs0[, model := gsub("sp_day_year", "species within day & year", model)]
xs0[, model := gsub("IDLocality", "site", model)]
xs0[, model := gsub("sp_loc", "species within site", model)]
g0_ =
ggplot(xs0[predictor == "scale(StringencyIndex)"], aes(y = model, x = estimate, col = model)) +
geom_vline(xintercept = 0, col = "grey30", lty = 3) +
geom_errorbar(aes(xmin = lwr, xmax = upr, col = model), width = 0, position = position_dodge(width = 0.01)) +
# ggtitle ("Sim based")+
geom_point(position = position_dodge(width = 0.01)) +
# scale_colour_brewer(type = 'qual', palette = 'Paired',guide = guide_legend(reverse = TRUE))+
# scale_fill_brewer(type = 'qual', palette = 'Paired',guide = guide_legend(reverse = TRUE))+
scale_color_viridis(discrete = TRUE, guide = guide_legend(reverse = TRUE)) +
scale_fill_viridis(discrete = TRUE, guide = guide_legend(reverse = TRUE)) +
scale_y_discrete(limits = rev) +
coord_fixed(ratio = 0.05, xlim = c(-0.23, 0.15)) +
# scale_shape(guide = guide_legend(reverse = TRUE)) +
# scale_x_continuous(limits = c(-2, 2), expand = c(0, 0), breaks = seq(-2,2, by = 1), labels = seq(-2,2, by = 1)) +
labs(y = NULL, x = "Stringency index", tag = "b)") + # title = "b) Effect of ") +
# ylim(c(0,100))+
# coord_flip()+
theme_bw() +
theme(
legend.position = "none",
plot.subtitle = element_text(size = 7),
plot.title = element_text(size = 7),
plot.tag = element_text(size = 7),
legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
## legend.spacing.y = unit(0.1, 'cm'),
legend.key.height = unit(0.5, "line"),
# plot.margin = margin(b = 0.5, l = 0.5, t = 0.5, r =0.5, unit = "pt"),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = ax_lines, size = 0.25),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.x = element_line(colour = ax_lines, size = 0.25),
axis.ticks.length = unit(1, "pt"),
axis.text.x = element_text(colour = "black", size = 6),
axis.text.y = element_text(colour = "black", size = 7),
axis.title = element_text(size = 7)
)
#g0
# ggsave(here::here('Outputs/Figure_Sz.png'),g0, width = 30, height =5, units = 'cm')
# prepare plot for Google
xg0 = rbind(est_mg01a, est_mg01b, est_mg01c, est_mg02a, est_mg02b, est_mg02c, est_mg03a, est_mg03b, est_mg03c)
xg0[, model := gsub("parks_percent_change_from_baseline", "Google Mobility", model)]
xg0[, model := gsub("Year", "year", model)]
xg0[, model := gsub("Species", "species", model)]
xg0[, model := gsub("sp_day_year", "species within day & year", model)]
xg0[, model := gsub("IDLocality", "site", model)]
xg0[, model := gsub("sp_loc", "species within site", model)]
gg0_ =
ggplot(xg0[predictor == "scale(parks_percent_change_from_baseline)"], aes(y = model, x = estimate, col = model)) +
geom_vline(xintercept = 0, col = "grey30", lty = 3) +
geom_errorbar(aes(xmin = lwr, xmax = upr, col = model), width = 0, position = position_dodge(width = 0.01)) +
# ggtitle ("Sim based")+
geom_point(position = position_dodge(width = 0.01)) +
# scale_colour_brewer(type = 'qual', palette = 'Paired',guide = guide_legend(reverse = TRUE))+
# scale_fill_brewer(type = 'qual', palette = 'Paired',guide = guide_legend(reverse = TRUE))+
scale_color_viridis(discrete = TRUE, guide = guide_legend(reverse = TRUE)) +
scale_fill_viridis(discrete = TRUE, guide = guide_legend(reverse = TRUE)) +
scale_y_discrete(limits = rev) +
coord_fixed(ratio = 0.05, xlim = c(-0.23, 0.15)) +
# scale_shape(guide = guide_legend(reverse = TRUE)) +
# scale_x_continuous(limits = c(-2, 2), expand = c(0, 0), breaks = seq(-2,2, by = 1), labels = seq(-2,2, by = 1)) +
labs(y = NULL, x = "Google Mobility\n[Standardized effect sizes on\nflight initiation distances]", tag = "c)") + # title = "b) Effect of ") +
# ylim(c(0,100))+
# coord_flip()+
theme_bw() +
theme(
legend.position = "none",
plot.subtitle = element_text(size = 7),
plot.title = element_text(size = 7),
plot.tag = element_text(size = 7),
legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
## legend.spacing.y = unit(0.1, 'cm'),
legend.key.height = unit(0.5, "line"),
# plot.margin = margin(b = 0.5, l = 0.5, t = 0.5, r =0.5, unit = "pt"),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = ax_lines, size = 0.25),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.x = element_line(colour = ax_lines, size = 0.25),
axis.ticks.length = unit(1, "pt"),
axis.text.x = element_text(colour = "black", size = 6),
axis.text.y = element_text(colour = "black", size = 7),
axis.title = element_text(size = 7)
)
#gg0
# ggsave(here::here('Outputs/Figure_Sz.png'),g0, width = 30, height =5, units = 'cm')
# combine
grid.draw(rbind(ggplotGrob(gs2_), ggplotGrob(g0_), ggplotGrob(gg0_)))
if(save_plot==TRUE){
ggsave(here::here("Outputs/Fig_S2_rev_v6.png"), rbind(ggplotGrob(gs2_), ggplotGrob(g0_), ggplotGrob(gg0_)), width = 30, height = 15, units = "cm")
}Supplementary TABLES
Table S1 | Alternative models on escape distance given Period
m1a_ = m_out(name = "Table S1 - 1a", dep = "Escape distance", model = m1a, nsim = 5000)
m1b_ = m_out(name = "Table S1 - 1b", dep = "Escape distance", model = m1b, nsim = 5000)
m1c_ = m_out(name = "Table S1 - 1c", dep = "Escape distance", model = m1c, nsim = 5000)
m1d_ = m_out(name = "Table S1 - 1d", dep = "Escape distance", model = m1d, nsim = 5000)
m1e_ = m_out(name = "Table S1 - 1e", dep = "Escape distance", model = m1e, nsim = 5000)
m2a_ = m_out(name = "Table S1 - 2a", dep = "Escape distance", model = m2a, nsim = 5000)
m2b_ = m_out(name = "Table S1 - 2b", dep = "Escape distance", model = m2b, nsim = 5000)
m2c_ = m_out(name = "Table S1 - 2c", dep = "Escape distance", model = m2c, nsim = 5000)
m3a_ = m_out(name = "Table S1 - 3a", dep = "Escape distance", model = m3a, nsim = 5000)
m3b_ = m_out(name = "Table S1 - 3b", dep = "Escape distance", model = m3b, nsim = 5000)
m3c_ = m_out(name = "Table S1 - 3c", dep = "Escape distance", model = m3c, nsim = 5000)
out1 = rbind(m1a_, m1b_, m1c_, m1d_, m1e_, m2a_, m2b_, m2c_, m3a_, m3b_, m3c_, fill = TRUE)
out1[is.na(out1)] = ""
out1[, effect := gsub("Covid", "Period", effect)]
out1[, effect := gsub("Year", "year", effect)]
out1[, effect := gsub("Species", "species", effect)]
out1[, effect := gsub("sp_day_year", "species within day & year", effect)]
out1[, effect := gsub("IDLocality", "site", effect)]
out1[, effect := gsub("sp_loc", "species within site", effect)]
out1$R2_mar=out1$R2_con=NULL
fwrite(file = here::here("Outputs/Table_S1_rev.csv"), out1)
out1 %>%
kbl() %>%
kable_paper("hover", full_width = F)| model | response | error_structure | N | type | effect | estimate_r | lwr_r | upr_r |
|---|---|---|---|---|---|---|---|---|
| Table S1 - 1a | Escape distance | Gaussian | 6369 | fixed | (Intercept) | 0.082 | -0.128 | 0.288 |
| fixed | scale(log(SD)) | 0.477 | 0.454 | 0.501 | ||||
| fixed | scale(log(FlockSize)) | -0.008 | -0.027 | 0.011 | ||||
| fixed | scale(log(BodyMass)) | 0.046 | -0.02 | 0.114 | ||||
| fixed | scale(sin(rad)) | 0.022 | -0.005 | 0.049 | ||||
| fixed | scale(cos(rad)) | 0.029 | 0.006 | 0.051 | ||||
| fixed | scale(Temp) | 0 | -0.029 | 0.028 | ||||
| fixed | scale(Period) | -0.033 | -0.191 | 0.13 | ||||
| random % | species within day & year (Intercept) | 8% | 6% | 9% | ||||
| random % | species within site (Intercept) | 5% | 4% | 5% | ||||
| random % | site (Intercept) | 1% | 0% | 5% | ||||
| random % | site scale(Period) | 1% | 0% | 5% | ||||
| random % | species (Intercept) | 9% | 8% | 8% | ||||
| random % | genus (Intercept) | 6% | 6% | 6% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 1% | -3% | 5% | ||||
| random % | Country scale(Period) | 1% | -3% | 5% | ||||
| random % | year (Intercept) | 3% | 1% | 4% | ||||
| random % | Residual | 65% | 51% | 76% | ||||
| Table S1 - 1b | Escape distance | Gaussian | 6369 | fixed | (Intercept) | 0.069 | -0.154 | 0.291 |
| fixed | scale(log(SD)) | 0.478 | 0.455 | 0.503 | ||||
| fixed | scale(log(FlockSize)) | -0.007 | -0.026 | 0.012 | ||||
| fixed | scale(log(BodyMass)) | 0.046 | -0.019 | 0.112 | ||||
| fixed | scale(sin(rad)) | 0.02 | -0.009 | 0.049 | ||||
| fixed | scale(cos(rad)) | 0.027 | 0.005 | 0.051 | ||||
| fixed | scale(Temp) | -0.001 | -0.029 | 0.028 | ||||
| fixed | scale(Period) | -0.047 | -0.209 | 0.111 | ||||
| random % | species within day & year (Intercept) | 12% | 7% | 14% | ||||
| random % | species within site (Intercept) | 0% | -1% | 0% | ||||
| random % | species within site scale(Period) | 0% | -1% | 0% | ||||
| random % | site (Intercept) | 2% | 0% | 5% | ||||
| random % | site scale(Period) | 2% | 0% | 5% | ||||
| random % | species (Intercept) | 0% | -1% | 8% | ||||
| random % | species scale(Period) | 0% | -1% | 8% | ||||
| random % | genus (Intercept) | 0% | 0% | 5% | ||||
| random % | genus scale(Period) | 0% | 0% | 5% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 1% | -4% | 6% | ||||
| random % | Country scale(Period) | 1% | -4% | 6% | ||||
| random % | year (Intercept) | 3% | 2% | 4% | ||||
| random % | Residual | 79% | 42% | 94% | ||||
| Table S1 - 1c | Escape distance | Gaussian | 6369 | fixed | (Intercept) | 0.013 | -0.161 | 0.175 |
| fixed | scale(log(SD)) | 0.494 | 0.469 | 0.518 | ||||
| fixed | scale(log(FlockSize)) | -0.018 | -0.037 | 0 | ||||
| fixed | scale(log(BodyMass)) | 0.025 | -0.006 | 0.054 | ||||
| fixed | scale(sin(rad)) | 0.014 | -0.014 | 0.043 | ||||
| fixed | scale(cos(rad)) | 0.027 | 0.003 | 0.051 | ||||
| fixed | scale(Temp) | -0.004 | -0.034 | 0.027 | ||||
| fixed | scale(Period) | -0.041 | -0.212 | 0.132 | ||||
| random % | species within day & year (Intercept) | 12% | 7% | 13% | ||||
| random % | species within site (Intercept) | 1% | 0% | 12% | ||||
| random % | species within site scale(Period) | 1% | 0% | 12% | ||||
| random % | site (Intercept) | 1% | 1% | 6% | ||||
| random % | site scale(Period) | 1% | 1% | 6% | ||||
| random % | species scale(Period) | 1% | 1% | 1% | ||||
| random % | genus scale(Period) | 0% | 0% | 0% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 1% | -2% | 4% | ||||
| random % | Country scale(Period) | 1% | -2% | 4% | ||||
| random % | year (Intercept) | 3% | 2% | 4% | ||||
| random % | Residual | 77% | 46% | 87% | ||||
| Table S1 - 1d | Escape distance | Gaussian | 6369 | fixed | (Intercept) | 0.083 | -0.124 | 0.286 |
| fixed | scale(log(SD)) | 0.476 | 0.452 | 0.501 | ||||
| fixed | scale(log(FlockSize)) | -0.008 | -0.026 | 0.011 | ||||
| fixed | scale(log(BodyMass)) | 0.046 | -0.018 | 0.114 | ||||
| fixed | scale(sin(rad)) | 0.022 | -0.007 | 0.049 | ||||
| fixed | scale(cos(rad)) | 0.028 | 0.006 | 0.051 | ||||
| fixed | scale(Temp) | 0 | -0.029 | 0.029 | ||||
| fixed | scale(Period) | -0.031 | -0.199 | 0.129 | ||||
| random % | species within day & year (Intercept) | 8% | 6% | 9% | ||||
| random % | species within site (Intercept) | 0% | 0% | 4% | ||||
| random % | species within site scale(Period) | 0% | 0% | 4% | ||||
| random % | site (Intercept) | 1% | 0% | 5% | ||||
| random % | site scale(Period) | 1% | 0% | 5% | ||||
| random % | species (Intercept) | 10% | 9% | 10% | ||||
| random % | genus (Intercept) | 7% | 6% | 6% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 1% | -3% | 5% | ||||
| random % | Country scale(Period) | 1% | -3% | 5% | ||||
| random % | year (Intercept) | 3% | 2% | 4% | ||||
| random % | Residual | 66% | 48% | 78% | ||||
| Table S1 - 1e | Escape distance | Gaussian | 6369 | fixed | (Intercept) | 0.077 | -0.127 | 0.283 |
| fixed | scale(log(SD)) | 0.478 | 0.454 | 0.501 | ||||
| fixed | scale(log(FlockSize)) | -0.008 | -0.027 | 0.011 | ||||
| fixed | scale(log(BodyMass)) | 0.048 | -0.021 | 0.114 | ||||
| fixed | scale(sin(rad)) | 0.021 | -0.006 | 0.048 | ||||
| fixed | scale(cos(rad)) | 0.024 | 0.002 | 0.047 | ||||
| fixed | scale(Temp) | -0.006 | -0.033 | 0.022 | ||||
| fixed | scale(Period) | -0.029 | -0.204 | 0.142 | ||||
| random % | species within day & year (Intercept) | 7% | 6% | 8% | ||||
| random % | species within site (Intercept) | 5% | 4% | 5% | ||||
| random % | site (Intercept) | 6% | 6% | 6% | ||||
| random % | species (Intercept) | 8% | 8% | 9% | ||||
| random % | genus (Intercept) | 6% | 5% | 7% | ||||
| random % | weekday (Intercept) | 0% | 0% | 1% | ||||
| random % | Country (Intercept) | 1% | -3% | 5% | ||||
| random % | Country scale(Period) | 1% | -3% | 5% | ||||
| random % | year (Intercept) | 2% | 1% | 4% | ||||
| random % | Residual | 62% | 52% | 71% | ||||
| Table S1 - 2a | Escape distance | Gaussian | 5261 | fixed | (Intercept) | 0.161 | -0.185 | 0.509 |
| fixed | scale(log(SD)) | 0.463 | 0.437 | 0.49 | ||||
| fixed | scale(log(FlockSize)) | -0.01 | -0.031 | 0.011 | ||||
| fixed | scale(log(BodyMass)) | -0.016 | -0.123 | 0.095 | ||||
| fixed | scale(sin(rad)) | 0.027 | -0.002 | 0.057 | ||||
| fixed | scale(cos(rad)) | 0.036 | 0.011 | 0.061 | ||||
| fixed | scale(Temp) | -0.005 | -0.039 | 0.026 | ||||
| fixed | scale(Period) | -0.035 | -0.204 | 0.133 | ||||
| random % | species within day & year (Intercept) | 7% | 4% | 9% | ||||
| random % | species within site (Intercept) | 3% | 2% | 4% | ||||
| random % | site (Intercept) | 1% | -1% | 2% | ||||
| random % | site scale(Period) | 1% | -1% | 2% | ||||
| random % | species (Intercept) | 10% | 8% | 9% | ||||
| random % | genus (Intercept) | 5% | 4% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 3% | -5% | 20% | ||||
| random % | Country scale(Period) | 3% | -5% | 20% | ||||
| random % | year (Intercept) | 3% | 2% | 3% | ||||
| random % | Residual | 64% | 34% | 84% | ||||
| Table S1 - 2b | Escape distance | Gaussian | 5261 | fixed | (Intercept) | 0.164 | -0.171 | 0.507 |
| fixed | scale(log(SD)) | 0.462 | 0.435 | 0.489 | ||||
| fixed | scale(log(FlockSize)) | -0.01 | -0.029 | 0.01 | ||||
| fixed | scale(log(BodyMass)) | -0.011 | -0.122 | 0.096 | ||||
| fixed | scale(sin(rad)) | 0.028 | -0.003 | 0.057 | ||||
| fixed | scale(cos(rad)) | 0.036 | 0.011 | 0.061 | ||||
| fixed | scale(Temp) | -0.005 | -0.037 | 0.028 | ||||
| fixed | scale(Period) | -0.036 | -0.204 | 0.146 | ||||
| random % | species within day & year (Intercept) | 9% | 4% | 11% | ||||
| random % | species within site (Intercept) | 0% | 0% | 2% | ||||
| random % | species within site scale(Period) | 0% | 0% | 2% | ||||
| random % | site (Intercept) | 1% | -1% | 2% | ||||
| random % | site scale(Period) | 1% | -1% | 2% | ||||
| random % | species (Intercept) | 0% | -1% | 5% | ||||
| random % | species scale(Period) | 0% | -1% | 5% | ||||
| random % | genus (Intercept) | 0% | 0% | 6% | ||||
| random % | genus scale(Period) | 0% | 0% | 6% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 3% | -6% | 17% | ||||
| random % | Country scale(Period) | 3% | -6% | 17% | ||||
| random % | year (Intercept) | 4% | 2% | 3% | ||||
| random % | Residual | 78% | 30% | 100% | ||||
| Table S1 - 2c | Escape distance | Gaussian | 5261 | fixed | (Intercept) | 0.153 | -0.197 | 0.513 |
| fixed | scale(log(SD)) | 0.464 | 0.437 | 0.492 | ||||
| fixed | scale(log(FlockSize)) | -0.01 | -0.03 | 0.011 | ||||
| fixed | scale(log(BodyMass)) | -0.01 | -0.114 | 0.101 | ||||
| fixed | scale(sin(rad)) | 0.026 | -0.002 | 0.055 | ||||
| fixed | scale(cos(rad)) | 0.032 | 0.007 | 0.057 | ||||
| fixed | scale(Temp) | -0.012 | -0.044 | 0.02 | ||||
| fixed | scale(Period) | -0.031 | -0.213 | 0.147 | ||||
| random % | species within day & year (Intercept) | 7% | 5% | 9% | ||||
| random % | species within site (Intercept) | 3% | 2% | 4% | ||||
| random % | site (Intercept) | 4% | 3% | 4% | ||||
| random % | species (Intercept) | 8% | 7% | 7% | ||||
| random % | genus (Intercept) | 6% | 5% | 6% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 2% | -6% | 17% | ||||
| random % | Country scale(Period) | 2% | -6% | 17% | ||||
| random % | year (Intercept) | 3% | 2% | 4% | ||||
| random % | Residual | 63% | 39% | 81% | ||||
| Table S1 - 3a | Escape distance | Gaussian | 5107 | fixed | (Intercept) | 0.137 | -0.205 | 0.493 |
| fixed | scale(log(SD)) | 0.462 | 0.435 | 0.488 | ||||
| fixed | scale(log(FlockSize)) | -0.01 | -0.031 | 0.01 | ||||
| fixed | scale(log(BodyMass)) | 0.003 | -0.104 | 0.114 | ||||
| fixed | scale(sin(rad)) | 0.028 | -0.003 | 0.058 | ||||
| fixed | scale(cos(rad)) | 0.034 | 0.01 | 0.06 | ||||
| fixed | scale(Temp) | -0.006 | -0.04 | 0.028 | ||||
| fixed | scale(Period) | -0.032 | -0.207 | 0.142 | ||||
| random % | species within day & year (Intercept) | 7% | 4% | 9% | ||||
| random % | species within site (Intercept) | 3% | 2% | 4% | ||||
| random % | site (Intercept) | 1% | -1% | 2% | ||||
| random % | site scale(Period) | 1% | -1% | 2% | ||||
| random % | species (Intercept) | 21% | 16% | 18% | ||||
| random % | genus (Intercept) | 0% | 0% | 0% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 2% | -5% | 19% | ||||
| random % | Country scale(Period) | 2% | -5% | 19% | ||||
| random % | year (Intercept) | 3% | 2% | 3% | ||||
| random % | Residual | 60% | 32% | 78% | ||||
| Table S1 - 3b | Escape distance | Gaussian | 5107 | fixed | (Intercept) | 0.139 | -0.21 | 0.484 |
| fixed | scale(log(SD)) | 0.461 | 0.433 | 0.487 | ||||
| fixed | scale(log(FlockSize)) | -0.01 | -0.031 | 0.01 | ||||
| fixed | scale(log(BodyMass)) | 0.011 | -0.102 | 0.126 | ||||
| fixed | scale(sin(rad)) | 0.027 | -0.002 | 0.057 | ||||
| fixed | scale(cos(rad)) | 0.034 | 0.009 | 0.06 | ||||
| fixed | scale(Temp) | -0.006 | -0.04 | 0.028 | ||||
| fixed | scale(Period) | -0.03 | -0.201 | 0.142 | ||||
| random % | species within day & year (Intercept) | 9% | 3% | 11% | ||||
| random % | species within site (Intercept) | 0% | 0% | 2% | ||||
| random % | species within site scale(Period) | 0% | 0% | 2% | ||||
| random % | site (Intercept) | 1% | -1% | 2% | ||||
| random % | site scale(Period) | 1% | -1% | 2% | ||||
| random % | species (Intercept) | 0% | -1% | 9% | ||||
| random % | species scale(Period) | 0% | -1% | 9% | ||||
| random % | genus (Intercept) | 0% | 0% | 3% | ||||
| random % | genus scale(Period) | 0% | 0% | 3% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 3% | -5% | 16% | ||||
| random % | Country scale(Period) | 3% | -5% | 16% | ||||
| random % | year (Intercept) | 4% | 2% | 3% | ||||
| random % | Residual | 78% | 28% | 100% | ||||
| Table S1 - 3c | Escape distance | Gaussian | 5107 | fixed | (Intercept) | 0.127 | -0.238 | 0.486 |
| fixed | scale(log(SD)) | 0.463 | 0.437 | 0.49 | ||||
| fixed | scale(log(FlockSize)) | -0.01 | -0.031 | 0.01 | ||||
| fixed | scale(log(BodyMass)) | 0.005 | -0.101 | 0.113 | ||||
| fixed | scale(sin(rad)) | 0.026 | -0.003 | 0.056 | ||||
| fixed | scale(cos(rad)) | 0.029 | 0.004 | 0.053 | ||||
| fixed | scale(Temp) | -0.014 | -0.046 | 0.018 | ||||
| fixed | scale(Period) | -0.027 | -0.205 | 0.15 | ||||
| random % | species within day & year (Intercept) | 7% | 5% | 9% | ||||
| random % | species within site (Intercept) | 3% | 2% | 4% | ||||
| random % | site (Intercept) | 5% | 3% | 5% | ||||
| random % | species (Intercept) | 15% | 11% | 15% | ||||
| random % | genus (Intercept) | 0% | 0% | 0% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 2% | -6% | 18% | ||||
| random % | Country scale(Period) | 2% | -6% | 18% | ||||
| random % | year (Intercept) | 3% | 2% | 4% | ||||
| random % | Residual | 62% | 38% | 78% |
Continuous variables were z-transformed (scale). Note that (1a) model is the one reported in the main text.
Table S2 | Alternative models on escape distance given Stringency
m01a_ = m_out(name = "Table S2 - 1a", dep = "Escape distance", model = m01a, nsim = 5000)
m01b_ = m_out(name = "Table S2 - 1b", dep = "Escape distance", model = m01b, nsim = 5000)
m01c_ = m_out(name = "Table S2 - 1c", dep = "Escape distance", model = m01c, nsim = 5000)
m02a_ = m_out(name = "Table S2 - 2a", dep = "Escape distance", model = m02a, nsim = 5000)
m02b_ = m_out(name = "Table S2 - 2b", dep = "Escape distance", model = m02b, nsim = 5000)
m02c_ = m_out(name = "Table S2 - 2c", dep = "Escape distance", model = m02c, nsim = 5000)
m03a_ = m_out(name = "Table S2 - 3a", dep = "Escape distance", model = m03a, nsim = 5000)
m03b_ = m_out(name = "Table S2 - 3b", dep = "Escape distance", model = m03b, nsim = 5000)
m03c_ = m_out(name = "Table S2 - 3c", dep = "Escape distance", model = m03c, nsim = 5000)
out2 = rbind(m01a_, m01b_, m01c_, m02a_, m02b_, m02c_, m03a_, m03b_, m03c_, fill = TRUE)
out2[is.na(out2)] = ""
out2[, effect := gsub("StringencyIndex", "Stringency Index", effect)]
out2[, effect := gsub("Year", "year", effect)]
out2[, effect := gsub("Species", "species", effect)]
out2[, effect := gsub("sp_day_year", "species within day & year", effect)]
out2[, effect := gsub("IDLocality", "site", effect)]
out2[, effect := gsub("sp_loc", "species within site", effect)]
out2$R2_mar = out2$R2_con = NULL
fwrite(file = here::here("Outputs/Table_S2_rev.csv"), out2)
out2 %>%
kbl() %>%
kable_paper("hover", full_width = F)| model | response | error_structure | N | type | effect | estimate_r | lwr_r | upr_r |
|---|---|---|---|---|---|---|---|---|
| Table S2 - 1a | Escape distance | Gaussian | 3676 | fixed | (Intercept) | -0.075 | -0.329 | 0.177 |
| fixed | scale(year) | 0.019 | -0.03 | 0.065 | ||||
| fixed | scale(log(SD)) | 0.492 | 0.46 | 0.523 | ||||
| fixed | scale(log(FlockSize)) | -0.004 | -0.029 | 0.024 | ||||
| fixed | scale(log(BodyMass)) | 0.022 | -0.046 | 0.091 | ||||
| fixed | scale(sin(rad)) | -0.02 | -0.061 | 0.022 | ||||
| fixed | scale(cos(rad)) | -0.005 | -0.04 | 0.029 | ||||
| fixed | scale(Temp) | -0.007 | -0.048 | 0.033 | ||||
| fixed | scale(Stringency Index) | 0.014 | -0.157 | 0.178 | ||||
| random % | species within day & year (Intercept) | 5% | 3% | 7% | ||||
| random % | species within site (Intercept) | 5% | 3% | 6% | ||||
| random % | species (Intercept) | 7% | 6% | 9% | ||||
| random % | site (Intercept) | 8% | 7% | 9% | ||||
| random % | genus (Intercept) | 4% | 4% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 2% | -15% | 16% | ||||
| random % | Country scale(Stringency Index) | 2% | -15% | 16% | ||||
| random % | Residual | 67% | 45% | 95% | ||||
| Table S2 - 1b | Escape distance | Gaussian | 3676 | fixed | (Intercept) | -0.074 | -0.329 | 0.181 |
| fixed | scale(year) | 0.019 | -0.029 | 0.066 | ||||
| fixed | scale(log(SD)) | 0.493 | 0.461 | 0.525 | ||||
| fixed | scale(log(FlockSize)) | -0.003 | -0.029 | 0.023 | ||||
| fixed | scale(log(BodyMass)) | 0.018 | -0.051 | 0.087 | ||||
| fixed | scale(sin(rad)) | -0.02 | -0.06 | 0.021 | ||||
| fixed | scale(cos(rad)) | -0.004 | -0.041 | 0.03 | ||||
| fixed | scale(Temp) | -0.008 | -0.051 | 0.035 | ||||
| fixed | scale(Stringency Index) | 0.008 | -0.161 | 0.17 | ||||
| random % | species within day & year (Intercept) | 5% | 3% | 7% | ||||
| random % | species within site (Intercept) | 5% | 3% | 7% | ||||
| random % | species (Intercept) | 7% | 5% | 8% | ||||
| random % | site (Intercept) | 8% | 6% | 10% | ||||
| random % | genus (Intercept) | 0% | -1% | 4% | ||||
| random % | genus scale(Stringency Index) | 0% | -1% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 2% | -16% | 15% | ||||
| random % | Country scale(Stringency Index) | 2% | -16% | 15% | ||||
| random % | Residual | 70% | 44% | 100% | ||||
| Table S2 - 1c | Escape distance | Gaussian | 3676 | fixed | (Intercept) | -0.109 | -0.277 | 0.06 |
| fixed | scale(year) | -0.003 | -0.051 | 0.045 | ||||
| fixed | scale(log(SD)) | 0.55 | 0.518 | 0.583 | ||||
| fixed | scale(log(FlockSize)) | -0.023 | -0.049 | 0.003 | ||||
| fixed | scale(log(BodyMass)) | -0.024 | -0.053 | 0.005 | ||||
| fixed | scale(sin(rad)) | -0.039 | -0.082 | 0.002 | ||||
| fixed | scale(cos(rad)) | 0.005 | -0.032 | 0.041 | ||||
| fixed | scale(Temp) | 0.003 | -0.039 | 0.045 | ||||
| fixed | scale(Stringency Index) | 0.019 | -0.134 | 0.176 | ||||
| random % | site (Intercept) | 10% | 10% | 10% | ||||
| random % | Country (Intercept) | 1% | -7% | 8% | ||||
| random % | Country scale(Stringency Index) | 1% | -7% | 8% | ||||
| random % | Residual | 88% | 74% | 103% | ||||
| Table S2 - 2a | Escape distance | Gaussian | 3573 | fixed | (Intercept) | -0.153 | -0.445 | 0.134 |
| fixed | scale(year) | 0.022 | -0.026 | 0.071 | ||||
| fixed | scale(log(SD)) | 0.483 | 0.451 | 0.515 | ||||
| fixed | scale(log(FlockSize)) | 0.002 | -0.024 | 0.03 | ||||
| fixed | scale(log(BodyMass)) | -0.024 | -0.099 | 0.053 | ||||
| fixed | scale(sin(rad)) | -0.02 | -0.061 | 0.021 | ||||
| fixed | scale(cos(rad)) | -0.008 | -0.044 | 0.028 | ||||
| fixed | scale(Temp) | -0.008 | -0.049 | 0.034 | ||||
| fixed | scale(Stringency Index) | 0.021 | -0.15 | 0.192 | ||||
| random % | species within day & year (Intercept) | 5% | 3% | 7% | ||||
| random % | species within site (Intercept) | 4% | 3% | 7% | ||||
| random % | site (Intercept) | 7% | 5% | 9% | ||||
| random % | species (Intercept) | 11% | 9% | 13% | ||||
| random % | genus (Intercept) | 2% | 2% | 2% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 3% | -20% | 20% | ||||
| random % | Country scale(Stringency Index) | 3% | -20% | 20% | ||||
| random % | Residual | 66% | 39% | 102% | ||||
| Table S2 - 2b | Escape distance | Gaussian | 3573 | fixed | (Intercept) | -0.153 | -0.439 | 0.135 |
| fixed | scale(year) | 0.021 | -0.028 | 0.07 | ||||
| fixed | scale(log(SD)) | 0.484 | 0.451 | 0.517 | ||||
| fixed | scale(log(FlockSize)) | 0.003 | -0.024 | 0.031 | ||||
| fixed | scale(log(BodyMass)) | -0.03 | -0.107 | 0.046 | ||||
| fixed | scale(sin(rad)) | -0.019 | -0.061 | 0.022 | ||||
| fixed | scale(cos(rad)) | -0.006 | -0.042 | 0.029 | ||||
| fixed | scale(Temp) | -0.008 | -0.051 | 0.033 | ||||
| fixed | scale(Stringency Index) | 0.019 | -0.152 | 0.185 | ||||
| random % | species within day & year (Intercept) | 5% | 3% | 8% | ||||
| random % | species within site (Intercept) | 4% | 3% | 7% | ||||
| random % | site (Intercept) | 7% | 5% | 9% | ||||
| random % | species (Intercept) | 11% | 8% | 13% | ||||
| random % | genus (Intercept) | 0% | -1% | 2% | ||||
| random % | genus scale(Stringency Index) | 0% | -1% | 2% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 3% | -21% | 19% | ||||
| random % | Country scale(Stringency Index) | 3% | -21% | 19% | ||||
| random % | Residual | 67% | 38% | 106% | ||||
| Table S2 - 2c | Escape distance | Gaussian | 3573 | fixed | (Intercept) | -0.107 | -0.288 | 0.07 |
| fixed | scale(year) | -0.001 | -0.049 | 0.048 | ||||
| fixed | scale(log(SD)) | 0.543 | 0.51 | 0.575 | ||||
| fixed | scale(log(FlockSize)) | -0.019 | -0.045 | 0.009 | ||||
| fixed | scale(log(BodyMass)) | -0.027 | -0.056 | 0.003 | ||||
| fixed | scale(sin(rad)) | -0.039 | -0.08 | 0.003 | ||||
| fixed | scale(cos(rad)) | 0.002 | -0.036 | 0.04 | ||||
| fixed | scale(Temp) | 0.002 | -0.04 | 0.046 | ||||
| fixed | scale(Stringency Index) | 0.024 | -0.14 | 0.193 | ||||
| random % | site (Intercept) | 10% | 10% | 10% | ||||
| random % | Country (Intercept) | 1% | -7% | 8% | ||||
| random % | Country scale(Stringency Index) | 1% | -7% | 8% | ||||
| random % | Residual | 88% | 73% | 105% | ||||
| Table S2 - 3a | Escape distance | Gaussian | 3425 | fixed | (Intercept) | -0.134 | -0.442 | 0.179 |
| fixed | scale(year) | 0.027 | -0.023 | 0.076 | ||||
| fixed | scale(log(SD)) | 0.481 | 0.448 | 0.515 | ||||
| fixed | scale(log(FlockSize)) | 0.002 | -0.024 | 0.029 | ||||
| fixed | scale(log(BodyMass)) | 0.024 | -0.067 | 0.11 | ||||
| fixed | scale(sin(rad)) | -0.011 | -0.054 | 0.032 | ||||
| fixed | scale(cos(rad)) | -0.009 | -0.045 | 0.028 | ||||
| fixed | scale(Temp) | -0.011 | -0.054 | 0.032 | ||||
| fixed | scale(Stringency Index) | 0.018 | -0.152 | 0.188 | ||||
| random % | species within day & year (Intercept) | 5% | 3% | 7% | ||||
| random % | species within site (Intercept) | 5% | 3% | 7% | ||||
| random % | site (Intercept) | 8% | 5% | 10% | ||||
| random % | species (Intercept) | 9% | 7% | 10% | ||||
| random % | genus (Intercept) | 3% | 3% | 3% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 2% | -20% | 20% | ||||
| random % | Country scale(Stringency Index) | 2% | -20% | 20% | ||||
| random % | Residual | 67% | 39% | 104% | ||||
| Table S2 - 3b | Escape distance | Gaussian | 3425 | fixed | (Intercept) | -0.138 | -0.449 | 0.171 |
| fixed | scale(year) | 0.026 | -0.025 | 0.076 | ||||
| fixed | scale(log(SD)) | 0.482 | 0.45 | 0.514 | ||||
| fixed | scale(log(FlockSize)) | 0.001 | -0.025 | 0.029 | ||||
| fixed | scale(log(BodyMass)) | 0.02 | -0.07 | 0.106 | ||||
| fixed | scale(sin(rad)) | -0.012 | -0.054 | 0.031 | ||||
| fixed | scale(cos(rad)) | -0.009 | -0.046 | 0.029 | ||||
| fixed | scale(Temp) | -0.01 | -0.053 | 0.032 | ||||
| fixed | scale(Stringency Index) | 0.015 | -0.157 | 0.194 | ||||
| random % | species within day & year (Intercept) | 5% | 3% | 7% | ||||
| random % | species within site (Intercept) | 4% | 3% | 7% | ||||
| random % | site (Intercept) | 8% | 5% | 11% | ||||
| random % | species (Intercept) | 10% | 7% | 12% | ||||
| random % | genus (Intercept) | 0% | 0% | 2% | ||||
| random % | genus scale(Stringency Index) | 0% | 0% | 2% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 3% | -23% | 21% | ||||
| random % | Country scale(Stringency Index) | 3% | -23% | 21% | ||||
| random % | Residual | 67% | 36% | 111% | ||||
| Table S2 - 3c | Escape distance | Gaussian | 3425 | fixed | (Intercept) | -0.106 | -0.292 | 0.071 |
| fixed | scale(year) | 0.005 | -0.044 | 0.055 | ||||
| fixed | scale(log(SD)) | 0.541 | 0.508 | 0.575 | ||||
| fixed | scale(log(FlockSize)) | -0.02 | -0.046 | 0.007 | ||||
| fixed | scale(log(BodyMass)) | -0.019 | -0.049 | 0.011 | ||||
| fixed | scale(sin(rad)) | -0.031 | -0.074 | 0.012 | ||||
| fixed | scale(cos(rad)) | -0.001 | -0.039 | 0.039 | ||||
| fixed | scale(Temp) | -0.002 | -0.045 | 0.042 | ||||
| fixed | scale(Stringency Index) | 0.022 | -0.145 | 0.18 | ||||
| random % | site (Intercept) | 11% | 11% | 11% | ||||
| random % | Country (Intercept) | 1% | -8% | 8% | ||||
| random % | Country scale(Stringency Index) | 1% | -8% | 8% | ||||
| random % | Residual | 88% | 73% | 105% |
Continuous variables were z-transformed (scale). Note that (1a) model is the one reported in the main text.
Table S3 | Alternative models on escape distance given Google Mobility
# google
mg01a_ = m_out(name = "Table S3 - 1a", dep = "Escape distance", model = mg01a, nsim = 5000)
mg01b_ = m_out(name = "Table S3 - 1b", dep = "Escape distance", model = mg01b, nsim = 5000)
mg01c_ = m_out(name = "Table S3 - 1c", dep = "Escape distance", model = mg01c, nsim = 5000)## Random effect variances not available. Returned R2 does not account for random effects.
## Random effect variances not available. Returned R2 does not account for random effects. mg02a_ = m_out(name = "Table S3 - 2a", dep = "Escape distance", model = mg02a, nsim = 5000)
mg02b_ = m_out(name = "Table S3 - 2b", dep = "Escape distance", model = mg02b, nsim = 5000)
mg02c_ = m_out(name = "Table S3 - 2c", dep = "Escape distance", model = mg02c, nsim = 5000)## Random effect variances not available. Returned R2 does not account for random effects.
## Random effect variances not available. Returned R2 does not account for random effects. mg03a_ = m_out(name = "Table S3 - 3a", dep = "Escape distance", model = mg03a, nsim = 5000)
mg03b_ = m_out(name = "Table S3 - 3b", dep = "Escape distance", model = mg03b, nsim = 5000)
mg03c_ = m_out(name = "Table S3 - 3c", dep = "Escape distancey", model = mg03c, nsim = 5000)## Random effect variances not available. Returned R2 does not account for random effects.
## Random effect variances not available. Returned R2 does not account for random effects. out3 = rbind(mg01a_, mg01b_, mg01c_, mg02a_, mg02b_, mg02c_, mg03a_, mg03b_, mg03c_, fill = TRUE)
out3[is.na(out3)] = ""
out3[, effect := gsub("parks_percent_change_from_baseline", "Google Mobility", effect)]
out3[, effect := gsub("Year", "year", effect)]
out3[, effect := gsub("Species", "species", effect)]
out3[, effect := gsub("sp_day_year", "species within day & year", effect)]
out3[, effect := gsub("IDLocality", "site", effect)]
out3[, effect := gsub("sp_loc", "species within site", effect)]
out3$R2_mar = out3$R2_con = NULL
fwrite(file = here::here("Outputs/Table_S3_rev.csv"), out3)
out3 %>%
kbl() %>%
kable_paper("hover", full_width = F)| model | response | error_structure | N | type | effect | estimate_r | lwr_r | upr_r |
|---|---|---|---|---|---|---|---|---|
| Table S3 - 1a | Escape distance | Gaussian | 3399 | fixed | (Intercept) | -0.015 | -0.169 | 0.141 |
| fixed | scale(year) | 0.027 | -0.017 | 0.071 | ||||
| fixed | scale(log(SD)) | 0.487 | 0.455 | 0.52 | ||||
| fixed | scale(log(FlockSize)) | 0.002 | -0.024 | 0.029 | ||||
| fixed | scale(log(BodyMass)) | 0.026 | -0.061 | 0.113 | ||||
| fixed | scale(sin(rad)) | 0.001 | -0.04 | 0.044 | ||||
| fixed | scale(cos(rad)) | 0.008 | -0.027 | 0.042 | ||||
| fixed | scale(Temp) | 0.022 | -0.017 | 0.06 | ||||
| fixed | scale(Google Mobility) | -0.108 | -0.232 | 0.016 | ||||
| random % | species within day & year (Intercept) | 6% | 5% | 6% | ||||
| random % | species within site (Intercept) | 4% | 4% | 5% | ||||
| random % | site (Intercept) | 9% | 9% | 9% | ||||
| random % | species (Intercept) | 7% | 6% | 9% | ||||
| random % | genus (Intercept) | 3% | 3% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 0% | -6% | 5% | ||||
| random % | Country scale(Google Mobility) | 0% | -6% | 5% | ||||
| random % | Residual | 70% | 60% | 82% | ||||
| Table S3 - 1b | Escape distance | Gaussian | 3399 | fixed | (Intercept) | -0.013 | -0.165 | 0.144 |
| fixed | scale(year) | 0.026 | -0.017 | 0.07 | ||||
| fixed | scale(log(SD)) | 0.487 | 0.456 | 0.522 | ||||
| fixed | scale(log(FlockSize)) | 0.002 | -0.026 | 0.03 | ||||
| fixed | scale(log(BodyMass)) | 0.032 | -0.051 | 0.119 | ||||
| fixed | scale(sin(rad)) | 0 | -0.041 | 0.043 | ||||
| fixed | scale(cos(rad)) | 0.006 | -0.028 | 0.042 | ||||
| fixed | scale(Temp) | 0.023 | -0.017 | 0.064 | ||||
| fixed | scale(Google Mobility) | -0.111 | -0.237 | 0.016 | ||||
| random % | species within day & year (Intercept) | 6% | 5% | 7% | ||||
| random % | species within site (Intercept) | 5% | 4% | 5% | ||||
| random % | site (Intercept) | 9% | 9% | 9% | ||||
| random % | species (Intercept) | 9% | 8% | 9% | ||||
| random % | genus (Intercept) | 0% | -1% | 4% | ||||
| random % | genus scale(Google Mobility) | 0% | -1% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 0% | -7% | 5% | ||||
| random % | Country scale(Google Mobility) | 0% | -7% | 5% | ||||
| random % | Residual | 71% | 56% | 86% | ||||
| Table S3 - 1c | Escape distance | Gaussian | 3399 | fixed | (Intercept) | -0.061 | -0.15 | 0.024 |
| fixed | scale(year) | 0.022 | -0.017 | 0.062 | ||||
| fixed | scale(log(SD)) | 0.549 | 0.517 | 0.582 | ||||
| fixed | scale(log(FlockSize)) | -0.018 | -0.046 | 0.008 | ||||
| fixed | scale(log(BodyMass)) | -0.019 | -0.048 | 0.011 | ||||
| fixed | scale(sin(rad)) | -0.024 | -0.065 | 0.019 | ||||
| fixed | scale(cos(rad)) | 0.015 | -0.021 | 0.052 | ||||
| fixed | scale(Temp) | 0.03 | -0.009 | 0.071 | ||||
| fixed | scale(Google Mobility) | -0.092 | -0.206 | 0.026 | ||||
| random % | site (Intercept) | 11% | 9% | 12% | ||||
| random % | Country (Intercept) | 1% | -1% | 3% | ||||
| random % | Country scale(Google Mobility) | 1% | -1% | 3% | ||||
| random % | Residual | 88% | 82% | 92% | ||||
| Table S3 - 2a | Escape distance | Gaussian | 3399 | fixed | (Intercept) | -0.017 | -0.17 | 0.138 |
| fixed | scale(year) | 0.027 | -0.017 | 0.07 | ||||
| fixed | scale(log(SD)) | 0.487 | 0.454 | 0.52 | ||||
| fixed | scale(log(FlockSize)) | 0.002 | -0.024 | 0.03 | ||||
| fixed | scale(log(BodyMass)) | 0.026 | -0.062 | 0.113 | ||||
| fixed | scale(sin(rad)) | 0.001 | -0.04 | 0.043 | ||||
| fixed | scale(cos(rad)) | 0.007 | -0.028 | 0.042 | ||||
| fixed | scale(Temp) | 0.022 | -0.017 | 0.062 | ||||
| fixed | scale(Google Mobility) | -0.106 | -0.234 | 0.018 | ||||
| random % | species within day & year (Intercept) | 6% | 5% | 6% | ||||
| random % | species within site (Intercept) | 4% | 4% | 5% | ||||
| random % | site (Intercept) | 9% | 9% | 9% | ||||
| random % | species (Intercept) | 7% | 6% | 9% | ||||
| random % | genus (Intercept) | 3% | 3% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 0% | -6% | 5% | ||||
| random % | Country scale(Google Mobility) | 0% | -6% | 5% | ||||
| random % | Residual | 70% | 60% | 83% | ||||
| Table S3 - 2b | Escape distance | Gaussian | 3399 | fixed | (Intercept) | -0.014 | -0.167 | 0.14 |
| fixed | scale(year) | 0.027 | -0.016 | 0.07 | ||||
| fixed | scale(log(SD)) | 0.488 | 0.455 | 0.521 | ||||
| fixed | scale(log(FlockSize)) | 0.002 | -0.025 | 0.029 | ||||
| fixed | scale(log(BodyMass)) | 0.032 | -0.053 | 0.115 | ||||
| fixed | scale(sin(rad)) | 0 | -0.041 | 0.042 | ||||
| fixed | scale(cos(rad)) | 0.007 | -0.029 | 0.041 | ||||
| fixed | scale(Temp) | 0.023 | -0.018 | 0.063 | ||||
| fixed | scale(Google Mobility) | -0.108 | -0.235 | 0.021 | ||||
| random % | species within day & year (Intercept) | 6% | 5% | 7% | ||||
| random % | species within site (Intercept) | 5% | 4% | 5% | ||||
| random % | site (Intercept) | 9% | 9% | 9% | ||||
| random % | species (Intercept) | 9% | 8% | 9% | ||||
| random % | genus (Intercept) | 0% | -1% | 4% | ||||
| random % | genus scale(Google Mobility) | 0% | -1% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 0% | -6% | 5% | ||||
| random % | Country scale(Google Mobility) | 0% | -6% | 5% | ||||
| random % | Residual | 71% | 56% | 86% | ||||
| Table S3 - 2c | Escape distance | Gaussian | 3399 | fixed | (Intercept) | -0.06 | -0.145 | 0.03 |
| fixed | scale(year) | 0.022 | -0.018 | 0.063 | ||||
| fixed | scale(log(SD)) | 0.55 | 0.516 | 0.583 | ||||
| fixed | scale(log(FlockSize)) | -0.019 | -0.046 | 0.008 | ||||
| fixed | scale(log(BodyMass)) | -0.018 | -0.05 | 0.01 | ||||
| fixed | scale(sin(rad)) | -0.024 | -0.066 | 0.018 | ||||
| fixed | scale(cos(rad)) | 0.015 | -0.021 | 0.051 | ||||
| fixed | scale(Temp) | 0.03 | -0.008 | 0.069 | ||||
| fixed | scale(Google Mobility) | -0.091 | -0.206 | 0.023 | ||||
| random % | site (Intercept) | 11% | 10% | 12% | ||||
| random % | Country (Intercept) | 1% | -1% | 3% | ||||
| random % | Country scale(Google Mobility) | 1% | -1% | 3% | ||||
| random % | Residual | 88% | 82% | 92% | ||||
| Table S3 - 3a | Escape distance | Gaussian | 3399 | fixed | (Intercept) | -0.015 | -0.169 | 0.141 |
| fixed | scale(year) | 0.027 | -0.016 | 0.071 | ||||
| fixed | scale(log(SD)) | 0.487 | 0.455 | 0.521 | ||||
| fixed | scale(log(FlockSize)) | 0.002 | -0.024 | 0.029 | ||||
| fixed | scale(log(BodyMass)) | 0.026 | -0.058 | 0.112 | ||||
| fixed | scale(sin(rad)) | 0.001 | -0.042 | 0.042 | ||||
| fixed | scale(cos(rad)) | 0.008 | -0.028 | 0.042 | ||||
| fixed | scale(Temp) | 0.022 | -0.017 | 0.063 | ||||
| fixed | scale(Google Mobility) | -0.107 | -0.235 | 0.02 | ||||
| random % | species within day & year (Intercept) | 6% | 5% | 6% | ||||
| random % | species within site (Intercept) | 4% | 4% | 5% | ||||
| random % | site (Intercept) | 9% | 8% | 9% | ||||
| random % | species (Intercept) | 7% | 6% | 9% | ||||
| random % | genus (Intercept) | 3% | 3% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 0% | -6% | 5% | ||||
| random % | Country scale(Google Mobility) | 0% | -6% | 5% | ||||
| random % | Residual | 70% | 60% | 82% | ||||
| Table S3 - 3b | Escape distance | Gaussian | 3399 | fixed | (Intercept) | -0.011 | -0.161 | 0.144 |
| fixed | scale(year) | 0.027 | -0.017 | 0.071 | ||||
| fixed | scale(log(SD)) | 0.488 | 0.454 | 0.521 | ||||
| fixed | scale(log(FlockSize)) | 0.002 | -0.026 | 0.028 | ||||
| fixed | scale(log(BodyMass)) | 0.032 | -0.052 | 0.116 | ||||
| fixed | scale(sin(rad)) | 0 | -0.041 | 0.042 | ||||
| fixed | scale(cos(rad)) | 0.006 | -0.028 | 0.042 | ||||
| fixed | scale(Temp) | 0.023 | -0.016 | 0.063 | ||||
| fixed | scale(Google Mobility) | -0.109 | -0.237 | 0.017 | ||||
| random % | species within day & year (Intercept) | 6% | 5% | 7% | ||||
| random % | species within site (Intercept) | 5% | 4% | 5% | ||||
| random % | site (Intercept) | 9% | 9% | 9% | ||||
| random % | species (Intercept) | 9% | 8% | 9% | ||||
| random % | genus (Intercept) | 0% | -1% | 4% | ||||
| random % | genus scale(Google Mobility) | 0% | -1% | 4% | ||||
| random % | weekday (Intercept) | 0% | 0% | 0% | ||||
| random % | Country (Intercept) | 0% | -6% | 5% | ||||
| random % | Country scale(Google Mobility) | 0% | -6% | 5% | ||||
| random % | Residual | 71% | 56% | 86% | ||||
| Table S3 - 3c | Escape distancey | Gaussian | 3399 | fixed | (Intercept) | -0.061 | -0.149 | 0.026 |
| fixed | scale(year) | 0.022 | -0.017 | 0.064 | ||||
| fixed | scale(log(SD)) | 0.549 | 0.516 | 0.582 | ||||
| fixed | scale(log(FlockSize)) | -0.019 | -0.046 | 0.008 | ||||
| fixed | scale(log(BodyMass)) | -0.019 | -0.049 | 0.011 | ||||
| fixed | scale(sin(rad)) | -0.024 | -0.064 | 0.018 | ||||
| fixed | scale(cos(rad)) | 0.015 | -0.022 | 0.053 | ||||
| fixed | scale(Temp) | 0.03 | -0.01 | 0.07 | ||||
| fixed | scale(Google Mobility) | -0.092 | -0.205 | 0.024 | ||||
| random % | site (Intercept) | 11% | 9% | 12% | ||||
| random % | Country (Intercept) | 1% | -1% | 3% | ||||
| random % | Country scale(Google Mobility) | 1% | -1% | 3% | ||||
| random % | Residual | 88% | 82% | 92% |
Continuous variables were z-transformed (scale). Note that (1a) model is the one reported in the main text.
# TO DO modelAss
# END